ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to claims filed on 10/23/2025.
Claims 1-20 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 5, 7, 15, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Longo (US 2022/0276907 A1) in view of Featonby (US 11,360,795 B2) in view of Shetty (US 2023/0396487 A1) in view of Adam (US 2018/0364879 A1).
With regard to claim 1, Longo teaches:
A method for managing a data protection module, the method comprising: obtaining client metadata (CM) “The slice services 220a-n may store metadata that maps between client systems and block services 215” [Longo ¶ 39]. “A data block, therefore, is the raw data for a volume and may be the smallest addressable unit of data. The metadata layer 304 or the client layer 302 can break data into data blocks. The data blocks can then be stored on multiple block servers 312a-n” [Longo ¶ 49].
of a first client environment data protection module (CEDPM) and a second CEDPM, “In some embodiments, the volumes may exist in multiple different protection domains (CEDPMs). For example, volume 330a may exist in a first protection domain, volume 330b may exist in a second protection domain, and volume 330n may exist in a third protection domain” [Longo ¶ 68]. “As used herein, a "protection domain" refers to a single storage server node (CEDPM) in a storage system” [Longo ¶ 79].
wherein the CM comprises at least product configuration information; “Depending upon the particular embodiment, the API 137 may provide access to various telemetry data (e.g., performance, configuration and other system data) relating to the cluster 135 or components thereof” [Longo ¶ 33]. “Metadata layer 304 includes one or more metadata servers 310a-n. Performance managers 314a-n may be located on metadata servers 310a-n” [Longo ¶ 47]. “noted above, in some embodiments, a performance manager module (e.g., performance manager 138 shown in FIG. 1) may poll an API (e.g., API 137 shown in FIG. 1) of a distributed storage system (e.g., cluster 135 shown in FIG. 1) of which the storage node 200 is a part to obtain various telemetry data of the distributed storage system” [Longo ¶ 43].
identifying, based on the CM and for the first CEDPM and the second CEDPM, a first resource and a second resource that have been utilized over a defined period of time (DPOT); “The method comprises collecting, from a plurality of volumes (resources) on a per-volume basis, one or more real-time performance metrics for one or more compute processes executing on the one or more computer systems…” [Longo ¶ 4]. “In some embodiments, metrics may be collected on a periodic basis. The period may be static or may vary with operational conditions. In some embodiments the collection frequency is configurable, but defaults to every 500 ms. Collection happens simultaneously on each node (CEDPM) of a storage system, and occurs within the slice service of each storage node” [Longo ¶ 72]. “As used herein, a "protection domain" refers to a single storage server node in a storage system” [Longo ¶ 79 Examiner notes both the protection domains and the storage nodes referenced are considered CEDPMs]. “The performance manager 138 may locally process and/or aggregate the collected compute load parameters (e.g., latency, utilization, IOPS, SS load, Quality of Service (QoS) settings, etc.) over a period of time by data point values and/or by ranges of data point values and provide frequency information regarding the aggregated compute load parameters retrieved from the cluster 135 to the normalizing agent 230” [Longo ¶ 35].
obtaining, based on the CM and for the first CEDPM and the second CEDPM, a resource utilization value for each identified resource over the DPOT; “In some examples a cloud-based QoS system may collect, from a plurality of the volumes 330a, 330b, 330n, on a per-volume basis, one or more real-time performance metrics for one or more compute processes executing on the one or more computer systems. In some embodiments, the one or more compute processes comprises at least one of a client process, a bin synchronization process, a slice balancing process, or a cluster fault monitoring process executing on the volume. Further, in some examples the one or more real-time performance metrics comprises at least one of a percentage of processor utilization metric (resource utilization value), an input/output (I/O) throughput metric, an average I/O size metric, an average read latency, or an average write latency” [Longo ¶ 69]. “The performance manager 138 may locally process and/or aggregate the collected compute load parameters (e.g., latency, utilization, IOPS, SS load, Quality of Service (QoS) settings, etc.) over a period of time by data point values and/or by ranges of data point values and provide frequency information regarding the aggregated compute load parameters retrieved from the cluster 135 to the normalizing agent 230” [Longo ¶ 35].
deriving, based on the resource utilization value for each identified resource, an average resource utilization value for each identified resource; “Metrics, including system metrics, background process metrics, and client metrics, can be calculated over a period of time (e.g., 250 ms, 500 ms, 1 s, etc). Accordingly, different statistical values (e.g., a min, max, standard deviation, average, etc.) can be calculated for each metric. One or more of the metrics can be used to calculate a value that represents one or more compute load parameters of the storage system” [Longo ¶ 66]. “In some examples an exponential moving average (EMA) may be determined across the performance metrics. For example, a 30% EMA may be used such that recent samples of performance are only weighted at 30% versus all past historic samples being weighted at 70%” [Longo ¶ 76].
generating a holistic (set of parameters) profile for each CEDPM “The performance manager 138 may locally process and/or aggregate the collected compute load parameters (e.g., latency, utilization, IOPS, SS load, Quality of Service (QoS) settings, etc.) over a period of time by data point values and/or by ranges of data point values and provide frequency information regarding the aggregated compute load parameters retrieved from the cluster 135 to the normalizing agent 230” [Longo ¶ 34].“As those skilled in the art will appreciate various other types of telemetry data may be made available via the API 137, including, but not limited to measures of latency, utilization, and/or performance at various levels (e.g., the cluster level, the storage node level, or the storage node component level)” [Longo ¶ 33].
based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; “In some examples an instruction may be generated that causes a processor to use the real-time performance metrics and the inertia parameter to determine whether to transfer responsibility for a compute process on a first storage server node to a second storage server node” [Longo ¶ 80]. “Accordingly, different statistical values (e.g., a min, max, standard deviation, average, etc.) can be calculated for each metric. One or more of the metrics can be used to calculate a value that represents one or more compute load parameters of the storage system” [Longo ¶ 66].
wherein the vendor environment database maintains a data protection landscape; “Different volume servers 322 may be responsible for different volumes. In this case, redirector server 320 is used to redirect the client to a specific volume server 322. To client 308, redirector server 320 may represent a single point of contact. The first request from client 308a then is redirected to a specific volume server 322. For example, redirector server 320 may use a database of volumes to determine which volume server 322 is a primary volume server for the requested target name” [Longo ¶ 57]. “In the system 300 depicted in FIG. 3, the client 308a utilizes storage services of a volume 330a, a client 308b utilizes storage services of a volume 330b, and a client 308n utilizes storage services of a volume 330n. In some embodiments, the volumes may exist in multiple different protection domains” [Longo ¶ 68].
making a determination that the resource utilization value of the first resource exceeds a predetermined maximum resource utilization level, wherein a user of the first CEDPM utilizes the first resource on the first CEDPM; “In some embodiments, the inertial parameter(s) may be generated by applying one or more weighting factor(s) to the parameters determined in operation 415 and may be generated for one or more of the parameters collected and may be used to adjust the relative weight assigned to the parameters in subsequent processing … In some embodiments, the inertial parameter may be as part of a cost/benefit calculation to determine whether the costs of moving processes from a particular volume outweigh the potential benefits of moving the processes from the particular volume … In general, a higher inertial parameter indicates that there is less benefit to obtain by transferring the processes to a different volume … At block 425, it is determined whether the inertial parameter(s) for the respective volume(s) generated in block 420 are less than a threshold” [Longo ¶ 75-78, Fig. 4 Examiner notes that at block 425 if the outcome is yes then this indicates that the inertial parameter(s) exceeds the maximum]. “If, at block 425, the inertial parameter for a given volume is not less than the threshold then control passes back to block 410 and the process 400 continues to collect metrics for that volume. By contrast, if at operation 425 the inertial parameter for a given volume is less than the threshold then control passes to block 430 and a performance capacity of one or more protection domains in the system is determined” [Longo ¶ 79 Examiner notes the threshold is considered a maximum for the inertial parameter which is in part based on the resource utilization level].
generating, based on the determination, a recommendation to modify the resource utilization value of the first resource, “If, at operation 435, a protection domain does not have sufficient performance capacity to accept additional volumes, then control passes back to block 410 and the process 400 continues to collect metrics. By contrast, if at operation 435 a protection domain has sufficient performance capacity to accept additional volumes, then control passes to operation 440 and a load balancing algorithm is applied to rebalance compute load between available protection domains. In some examples an instruction may be generated that causes a processor to use the real-time performance metrics and the inertia parameter to determine whether to transfer responsibility for a compute process on a first storage server node to a second storage server node” [Longo ¶ 80].
wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization across a plurality of client environment data protection modules; “FIG. 4 is a flow diagram illustrating operations in a technique to implement dynamic load balancing by analyzing performance of volume to quality of service (QoS) in accordance with an embodiment of the present disclosure” [Longo ¶ 14]. “By contrast, if at operation 435 a protection domain has sufficient performance capacity to accept additional volumes, then control passes to operation 440 and a load balancing algorithm is applied to rebalance compute load between available protection domains” [Longo ¶ 79].
wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, “As noted above, in some embodiments, a performance manager module (e.g., performance manager 138 shown in FIG. 1) may poll an API (e.g., API 137 shown in FIG. 1) of a distributed storage system (e.g., cluster 135 shown in FIG. 1) of which the storage node 200 is a part to obtain various telemetry data of the distributed storage system. The telemetry data may represent performance metrics (utilization performance), configuration and other system data associated with various levels or layers of the cluster or the storage node 200. For example, metrics may be available for individual or groups of storage nodes (e.g., 136a-n), individual or groups of volumes 221, individual or groups of slice services 220, and/or individual or groups of block services 215” [Longo ¶ 43]. “In various embodiments described herein, an administrator (e.g., user 112) of a distributed storage system (e.g., cluster 135) or a managed service provider responsible for multiple distributed storage systems of the same or multiple customers may monitor various telemetry data of the distributed storage system or multiple distributed storage systems via a browser-based interface presented on computer system 110” [Longo ¶ 29]. “Performance manager 138 can be configured to periodically poll and/or monitor for compute load parameters of the cluster 135 via the API 137. In some examples the polling may be performed on static periodic intervals. In other examples the polling interval may vary based upon one or more parameters (e.g., load, capacity, etc.). Depending upon the particular implementation, the polling may be performed at a predetermined or configurable interval (e.g., X milliseconds or Y seconds).” [Longo ¶ 34].
Longo fails to teach wherein the resource utilization value is obtained by employing a first set of machine learning (ML) models; identifying, based on the CM and for the first CEDPM and the second CEDPM, a plurality of modifications that have been performed on each identified resource over the DPOT; generating a holistic profile for each CEDPM based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; storing the holistic profile of each CEDPM in a vendor environment database, making a determination that the resource utilization value of the first resource exceeds a predetermined maximum resource utilization level, generating, based on the determination, a recommendation to modify the resource utilization value of the first resource, wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization and sending the recommendation and at least a portion of the holistic profile of the first CEDPM to the user, wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, initiating a recommendation monitoring service to track whether the recommendation is implemented by the user, and implementing, by the user, the recommendation.
However, Featonby teaches:
wherein the resource utilization value is obtained by employing a first set of machine learning (ML) models; “In this way, the ML component 222 may generate resource-utilization models 224 (first set of ML models) for each workload category 220 using anonymized historical-utilization data 412 for workloads 406 hosted by VM instances 404 across the computing-resource network 110. The resource-utilization models 224 may be representative of resource-utilization data 138 for the workloads 406 that are included in the workload categories 220” [Featonby Col. 23 Lines 5-12]. “At 704, the optimization service 106 may receive utilization data 138 indicating a resource-utilization characteristic of the workload 136 during execution. The resource-utilization characteristic may indicate at least one of an amount of the computing resources consumed by the workload 126 or a type of the computing resources consumed by the workload 136” [Featonby Col. 27 Lines 47-53]. “The resource-utilization models 224 may be representative of resource-utilization data 138 for the workloads 406 that are included in the workload categories 220. In this way, when a new workload 136 needs to be categorized for purposes of identifying optimized VM instance types 130, the resource-utilization data 138 for the new workload 136 may be mapped to the resource-utilization model 224 that is "closest" or "most near" the fingerprint of the resource-utilization data 138 for the new workload 136” [Featonby Col. 23 Lines 9-18].
identifying, based on the CM and for the first CEDPM and the second CEDPM, a plurality of modifications that have been performed on each identified resource over the DPOT; “For example, the workload may have changed over time (e.g., software update, new features, increase in user traffic, etc.) that in tum results in different resource consumption characteristics of the workload. In light of such modifications or changes, the optimization service may continually, or periodically, analyze the resource-utilization characteristics of the workload and determine if resource consumption has changed significantly enough such that a new VM instance type is more appropriate for the workload than the current VM instance type” [Featonby Col. 6 Lines 10-20]. “For instance, the user 105 may provide an indication to the optimization service 106 that the workload 136 has undergone a configuration change (e.g., update, software change, traffic change, etc.) that will likely result in a change in the resource-utilization characteristics of the workload 136” [Featonby Col. 14-15 Lines 65-67, 1-3].
generating a holistic profile for each CEDPM “The clustering component 218 may generate or determine the workload categories 220 using various clustering or classification techniques” [Featonby Col. 22 Lines 3-5]. “To illustrate, one predefined workload category may be a "database workload category" and generally correspond to the resource-utilization characteristics for database workloads (e.g., low compute consumption, high storage consumption, etc.). Another predefined workload category may be a "compute heavy category" and generally correspond to the resource-utilization characteristics for computationally-biased workloads” [Featonby Col. 6-7 Lines 62-67, 1-2]. “In this way, performance metrics may be assigned to the underlying computing device on which a VM instance is provisioned to help determine an optimized VM instance type based on the computing device that is to be utilized” [Featonby Col. 7 Lines 49-53]. “As shown, the GUI 502 may present (holistic profiles) instance type 504, suitability 506, and explanations 508 for the recommended VM instance types 130. In the illustrated example, a first VM instance 510 may be storage optimized, have a suitability 506 of 4.5 out of 5 stars, and have an explanation 508 indicating that the VM instance type 510 delivers additional storage with sufficient compute for the workload 130…” [Featonby Col. 24 Lines 37-42].
based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; “In some examples, the optimization service 106 may further monitor the workload 136 for the life of the workload 136, and provide additional recommendation data 132 upon detecting events that result in a different VM instance type 130 being more optimized to support the workload than the current VM instance type 130 to which the VM instance 114 corresponds. For instance, the user 105 may provide an indication to the optimization service 106 that the workload 136 has undergone a configuration change (e.g., update, software change, traffic change, etc.) that will likely result in a change in the resource-utilization characteristics of the workload 136” [Featonby Col. 14-15 Lines 59-67, 1-3]. “For example, the optimization component 126 may determine that the resource-utilization characteristic (e.g., CPU, GPU, memory, disk, network throughput, etc.) may at least partly correspond or match to a resource-utilization model 224 for the workload category 220” [Featonby Col. 27 Lines 58-62].
storing the holistic profile of each CEDPM in a vendor environment database, “Additionally, the service provider network 102 may include a data store 208 which may comprise one, or multiple, repositories or other storage locations for persistently storing and managing collections of data such as databases, simple files, binary, and/or any other data. The data store 208 may include one or more storage locations that may be managed by one or more database management systems” [Featonby Col. 16 Lines 16-23, Fig. 2 Examiner notes the location of Workload Categories 220 within Data Store 208 in figure 2].
making a determination that the resource utilization value of the first resource exceeds a predetermined maximum resource utilization level, “At 1806, the service provider network 102 may determine that the actual utilization rate is different than a desired utilization rate specified for the user account” [Featonby Col. 38 Lines 10-12]. “To determine performance 1722, the optimization service 106 may compare throughput of data, such as overall utilization 1720) for the respective compute type (e.g., CPU, memory, disk, GPU, network throughput, etc.), versus the baseline and/or across the different chipset models. In this way, the optimization service 106 may determine how performant one chipset model is compared to another chipset model. Although illustrated as being CPU usage, the performance 1722 may be determined for one or more of the dimensions of compute (e.g., CPU, memory, disk, GPU, and network throughput) for the different chipset models and/or device IDs 1714 … As a specific example, a user account 242 may be hosting their workload 1706 on a first VM instance type 130(1) that is supported by a first chipset model 1714. However, that workload 1706 may be consuming too much CPU (and/or other computing resource) compared to a utilization goal or preference” [Featonby Col. 36-37 Lines 64-67, 1-9, 28-33].
generating, based on the determination, a recommendation to modify the resource utilization value of the first resource, wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization “The workload categories may further be associated with the VM instance types that are optimized for the resource-utilization characteristics of the represented workloads. In this way, when resource-utilization characteristics are obtained from a description provided by a new user, or through actual utilization data throughout the life of a workload, the resource-utilization characteristics may be mapped to the "closest" resource-utilization model of a predefined workload category, and the associated VM instance types for that workload category may be provided as recommendations to optimize performance of the workload” [Featonby Col. 7 Lines 12-23]. “In this way, performance metrics may be assigned to the underlying computing device on which a VM instance is provisioned to help determine an optimized VM instance type based on the computing device that is to be utilized. In an example where a workload is migrated from a less compute-performant device onto a more compute-performant device, the optimization service may select a new VM instance type based in part on a ratio of the performance between the two devices. In this way, the optimization service may select a new VM instance type that may not need be allocated as much compute power of the more compute-performance computing device” [Featonby Col. 7 Lines 49-60].
and sending the recommendation “FIG. 11 illustrates a flow diagram of an example method for determining that a new VM instance type is more optimized to support a workload than a current VM instance type, recommending the new VM instance type to a user account associated with the workload, and migrating the workload to the new VM instance type” [Featonby Col. 2 Lines 44-49].
and at least a portion of the holistic profile of the first CEDPM to the user, “As shown, the GUI 502 may present instance type 504, suitability 506, and explanations 508 for the recommended VM instance types 130. In the illustrated example, a first VM instance 510 may be storage optimized, have a suitability 506 of 4.5 out of 5 stars, and have an explanation 508 indicating that the VM instance type 510 delivers additional storage with sufficient compute for the workload 130” [Featonby Col. 24 Lines 37-43].
wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, “The GUI 300 may be presented on a user device 108, and accessible via a user account 242 and a console 122. In some examples, the GUI 300 may be part of the web-console wizard 212 that assists the user 105 in selecting an optimized or appropriate VM instance type 130 for a new workload 136” [Featonby Col. 19 Lines 62-67]. “The GUI 502 may comprise options through which a user 105 can select or choose a VM instance type 130. The GUI 502 may list different VM instances types 130 that have been determined by the optimization service 106 as being optimized for the workload 130 associated with the user account 242 through which the console 122 is accessed” [Featonby Col. 24 Lines 28-33]. “As a specific example, a user account 242 may be hosting their workload 1706 on a first VM instance type 130(1) that is supported by a first chipset model 1714. However, that 30 workload 1706 may be consuming too much CPU (and/or other computing resource) compared to a utilization goal or preference. The optimization service 106 may determine that a second VM instance type 130(2) that is supported by a second chipset model 1714 is more appropriate to host the workload 136 to achieve the utilization goal because, even if the second chipset model and second VM instance type 130(2) have less CPUs 1718 and/or vCPUs 1716, the ratio of performance metrics 1724 between the first and second chipset models 1714 may indicate that the second chipset model 1714 will still achieve lower utilization, and the same or higher throughput, to support the workload 1706. The optimization service 106 may select VM instance types 130 based at least in part on the performance metrics 1724 for the underlying computing resources of the supporting devices” [Featonby Col. 37 Lines 28-45].
initiating a recommendation monitoring service to track whether the recommendation is implemented by the user, and implementing, by the user, the recommendation, “Using this recommendation data 132, the user 105 can make a more informed decision as to what VM instance type 130 to utilize to support their workload 130, check a box next to the VM instance type 130 they desire, and further provide input into a select instance type control 516. Upon selecting the instance type, selection data 518 may be sent from the user device 108, over the network(s) 118, to the service provider network 102 (recommendation monitoring service) to indicate that the user 105 is requesting to have their workload 130 launched or supported by the first VM instance type 510” [Featonby Col. 24 Lines 53-62]. “The GUI 502 may allow the user 102 to select one of the VM instance types 130 to launch their workload 130 in an automated fashion” [Featonby Col. 24 Lines 34-36].
Featonby is considered to be analogous to the claimed invention because it is in the same field of hypervisor-specific management and integration aspects. Longo teaches a method of metadata monitoring where resource utilization data is computed and averaged then included in a set of resource parameters used in making optimal decisions for protection domain management. This can be combined with the teachings of Featonby to generate a profile with the resource utilization data and to provide optimal decisions as suggestions to users. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo to incorporate the teachings of Featonby and include wherein the resource utilization value is obtained by employing a first set of machine learning (ML) models; identifying, based on the CM and for the first CEDPM and the second CEDPM, a plurality of modifications that have been performed on each identified resource over the DPOT; generating a holistic profile for each CEDPM based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; storing the holistic profile of each CEDPM in a vendor environment database, making a determination that the resource utilization value of the first resource exceeds a predetermined maximum resource utilization level, generating, based on the determination, a recommendation to modify the resource utilization value of the first resource, wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization and sending the recommendation and at least a portion of the holistic profile of the first CEDPM to the user, wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, initiating a recommendation monitoring service to track whether the recommendation is implemented by the user, and implementing, by the user, the recommendation. Doing so would allow for decisions made for optimization to be provided as recommendations to users before they are implemented. “In this way, the optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network” [Featonby Col. 5 Lines 27-31].
Longo in view of Featonby fails to teach wherein the holistic profile for each CEDPM is generated by employing a second set of ML models.
However, Shetty teaches wherein the holistic profile for each CEDPM is generated by employing a second set of ML models; “In another example, if a new device is obtained by an organization, the hardware specs of the device can be inputted into the first ML model to generate a device profile. The device profile can be inputted into the third ML model to identify users or user groups to whom the new device is recommended” [Shetty ¶ 101].
Shetty is considered to be analogous to the claimed invention because it is in the same field of allocation of resources. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby to incorporate the teachings of Shetty and include wherein the holistic profile for each CEDPM is generated by employing a second set of ML models. Doing so would allow for the consideration of further CEDPM characteristics within the evaluations. “A second ML model can be trained to learn how a device is used by a user or users with similar roles. The second ML model can be trained using data related to the average lifespan of a device before replacement, software capabilities (maximum OS upgrade, support for certain apps, and so on), and general use cases of a device (e.g., the types of user to whom a particular device is assigned)” [Shetty ¶ 101].
Longo in view of Featonby in view of Shetty fails to explicitly teach wherein upon implementation, making the first CEDPM more user-friendly by reducing a number of steps required for subsequent operations.
However, Adam teaches wherein upon implementation, making the first CEDPM more user-friendly by reducing a number of steps required for subsequent operations. “And in some cases the system may automatically do certain actions that may no longer need explicit user interaction or automate them into a single step, especially when the prediction system for a particular user achieves a high level of reliability” [Adam ¶ 46]. “In step 806, user experience system 104 automates the next action. In automating the system is not presenting the action on the adapted user interface. Instead, it is performing the action for the user and then moving to the next predicted action and adapted user interface. Because of the decision in step 804, these actions are automated without risk for the software to perform an action not wanted by the user” [Adam ¶ 108].
Adam is considered to be analogous to the claimed invention because it is in the same field of interaction techniques based on graphical user interfaces. Longo in view of Featonby in view of Shetty presents a system wherein recommendations pertaining to CEPDMs are implemented by users. The teachings of Adam can be combined with this system to reduce a number of steps required for subsequent operations after the recommendation of Longo in view of Featonby in view of Shetty is implemented. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Shetty to incorporate the teachings of Adam and include wherein upon implementation, making the first CEDPM more user-friendly by reducing a number of steps required for subsequent operations. Doing so would allow for further ease of user for the user. “A less experienced user may not care to know the advanced features of a software application and may be using the application a few times to complete a straight forward task. Thus, the it is sometimes valuable to provide a simpler adapted user interface in some scenarios” [Adam ¶ 83].
With regard to claim 3, Longo in view of Featonby in view of Shetty in view of Adam teaches the method of claim 1, as referenced above. Longo further teaches:
wherein the first resource is a virtual machine, “As an example, a computer may be one or more server computers, cloud-based computers, cloud-based cluster of computers, virtual machine instances or virtual machine computing elements such as virtual processors, storage and memory, data centers, storage devices, desktop computers, laptop computers, mobile devices, or any other special-purpose computing devices” [Longo ¶ 22]. “Entities in system 300 may be virtualized entities. For example, multiple virtual block servers 312 may be included on a machine. Entities may also be included in a cluster, where computing resources of the cluster are virtualized such that the computing resources appear as a single entity” [Longo ¶ 55, Fig. 3C Examiner notes the volumes 330a-n in figure 3C].
wherein the virtual machine provides at least one computer-implemented service to the user. “Each slice service 220 may include one or more volumes (e.g., volumes 22la-x, volumes 22lc-y, and volumes 22le-z). Client systems (not shown) associated with an enterprise may store data to one or more volumes, retrieve data from one or more volumes, and/or modify data stored on one or more volumes” [Longo ¶ 38].
With regard to claim 5, Longo in view of Featonby in view of Shetty in view of Adam teaches the method of claim 1, as referenced above. Longo further teaches wherein the product configuration information specifies at least one selected from a group consisting of a type of each of a plurality of assets, a number of each type of the plurality of assets, a size of each of the plurality of assets, a type of an operating system, and a number of each type of a plurality of data protection policies. “In some examples a cloud-based QoS system may collect, from a plurality of the volumes 330a, 330b, 330n, on a per-volume basis, one or more real-time performance metrics for one or more compute processes executing on the one or more computer systems” [Longo ¶ 69]. “At block 415, at least one of a volume size, a volume activity level, or a number of client sessions operating on a volume is determined” [Longo ¶ 74].
With regard to claim 7, Longo in view of Featonby in view of Shetty in view of Adam teaches the method of claim 1, as referenced above. Longo further teaches:
wherein the recommendation specifies obtaining a third CEDPM “The performance metrics may be used to perform load balancing operations by shifting volume compute responsibilities (i.e., read/write, hashing, compression) between storage services and/or protection domains” [Longo ¶ 16]. “In some embodiments, the volumes may exist in multiple different protection domains (CEDPMs). For example, volume 330a may exist in a first protection domain, volume 330b may exist in a second protection domain, and volume 330n may exist in a third protection domain” [Longo ¶ 68].
in order to manage the resource utilization value of the first resource in the first CEDPM, wherein the first CEDPM is provided to the user by the vendor. “If, at operation 435, a protection domain does not have sufficient performance capacity to accept additional volumes, then control passes back to block 410 and the process 400 continues to collect metrics. By contrast, if at operation 435 a protection domain has sufficient performance capacity to accept additional volumes, then control passes to operation 440 and a load balancing algorithm is applied to rebalance compute load between available protection domains. In some examples an instruction may be generated that causes a processor to use the real-time performance metrics and the inertia parameter to determine whether to transfer responsibility for a compute process on a first storage server node to a second storage server node” [Longo ¶ 80].
Longo fails to teach wherein the recommendation specifies obtaining a third CEDPM from a vendor.
However, Featonby teaches wherein the recommendation specifies obtaining a third CEDPM from a vendor “In other examples, the service provider network may develop and offer new VM instance type(s) to increase the offerings of VM instance types for users. The optimization service may use various techniques, such as workload simulation, to determine that the new VM instance type is more optimized for the workload (or workload category to which the workload belongs) than the currently utilized VM instance type. For such reasons, and potentially other reasons, the optimization service may provide the user account with recommendations that the user migrate their workload from the current VM instance type to be hosted by a different VM instance type that is more optimized for the resource consumption/utilization of the workload” [Featonby Col. 6 Lines 20-33].
With regard to claim 15, Longo teaches:
A non-transitory computer-readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform “In another embodiment, a system comprises a processing resource and a non-transitory computer-readable medium, coupled to the processing resource, having stored therein instructions that when executed by the processing resource cause the processing resource to…” [Longo ¶ 5].
a method for managing a data protection module, the method comprising: obtaining client metadata (CM) “The slice services 220a-n may store metadata that maps between client systems and block services 215” [Longo ¶ 39]. “A data block, therefore, is the raw data for a volume and may be the smallest addressable unit of data. The metadata layer 304 or the client layer 302 can break data into data blocks. The data blocks can then be stored on multiple block servers 312a-n” [Longo ¶ 49].
of a first client environment data protection module (CEDPM) and a second CEDPM, “In some embodiments, the volumes may exist in multiple different protection domains (CEDPMs). For example, volume 330a may exist in a first protection domain, volume 330b may exist in a second protection domain, and volume 330n may exist in a third protection domain” [Longo ¶ 68]. “As used herein, a "protection domain" refers to a single storage server node (CEDPM) in a storage system” [Longo ¶ 79].
wherein the CM comprises at least product configuration information; “Depending upon the particular embodiment, the API 137 may provide access to various telemetry data (e.g., performance, configuration and other system data) relating to the cluster 135 or components thereof” [Longo ¶ 33]. “Metadata layer 304 includes one or more metadata servers 310a-n. Performance managers 314a-n may be located on metadata servers 310a-n” [Longo ¶ 47]. “noted above, in some embodiments, a performance manager module (e.g., performance manager 138 shown in FIG. 1) may poll an API (e.g., API 137 shown in FIG. 1) of a distributed storage system (e.g., cluster 135 shown in FIG. 1) of which the storage node 200 is a part to obtain various telemetry data of the distributed storage system” [Longo ¶ 43].
identifying, based on the CM and for the first CEDPM and the second CEDPM, a first resource and a second resource that have been utilized over a defined period of time (DPOT); “The method comprises collecting, from a plurality of volumes (resources) on a per-volume basis, one or more real-time performance metrics for one or more compute processes executing on the one or more computer systems…” [Longo ¶ 4]. “In some embodiments, metrics may be collected on a periodic basis. The period may be static or may vary with operational conditions. In some embodiments the collection frequency is configurable, but defaults to every 500 ms. Collection happens simultaneously on each node (CEDPM) of a storage system, and occurs within the slice service of each storage node” [Longo ¶ 72]. “As used herein, a "protection domain" refers to a single storage server node in a storage system” [Longo ¶ 79 Examiner notes both the protection domains and the storage nodes they refer to are considered one CEDPM]. “The performance manager 138 may locally process and/or aggregate the collected compute load parameters (e.g., latency, utilization, IOPS, SS load, Quality of Service (QoS) settings, etc.) over a period of time by data point values and/or by ranges of data point values and provide frequency information regarding the aggregated compute load parameters retrieved from the cluster 135 to the normalizing agent 230” [Longo ¶ 35].
obtaining, based on the CM and for the first CEDPM and the second CEDPM, a resource utilization value for each identified resource over the DPOT; “The performance manager 138 may locally process and/or aggregate the collected compute load parameters (e.g., latency, utilization, IOPS, SS load, Quality of Service (QoS) settings, etc.) over a period of time by data point values and/or by ranges of data point values and provide frequency information regarding the aggregated compute load parameters retrieved from the cluster 135 to the normalizing agent 230” [Longo ¶ 35]. “Further, in some examples the one or more real-time performance metrics comprises at least one of a percentage of processor utilization metric, an input/ output (I/O) throughput metric, an average I/O size metric, an average read latency, or an average write latency” [Longo ¶ 69].
deriving, based on the resource utilization value for each identified resource, an average resource utilization value for each identified resource; “Metrics, including system metrics, background process metrics, and client metrics, can be calculated over a period of time (e.g., 250 ms, 500 ms, 1 s, etc). Accordingly, different statistical values (e.g., a min, max, standard deviation, average, etc.) can be calculated for each metric. One or more of the metrics can be used to calculate a value that represents one or more compute load parameters of the storage system” [Longo ¶ 66]. “In some examples an exponential moving average (EMA) may be determined across the performance metrics. For example, a 30% EMA may be used such that recent samples of performance are only weighted at 30% versus all past historic samples being weighted at 70%” [Longo ¶ 76].
generating a holistic (set of parameters) profile for each CEDPM “The performance manager 138 may locally process and/or aggregate the collected compute load parameters (e.g., latency, utilization, IOPS, SS load, Quality of Service (QoS) settings, etc.) over a period of time by data point values and/or by ranges of data point values and provide frequency information regarding the aggregated compute load parameters retrieved from the cluster 135 to the normalizing agent 230” [Longo ¶ 34].“As those skilled in the art will appreciate various other types of telemetry data may be made available via the API 137, including, but not limited to measures of latency, utilization, and/or performance at various levels (e.g., the cluster level, the storage node level, or the storage node component level)” [Longo ¶ 33].
based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; “In some examples an instruction may be generated that causes a processor to use the real-time performance metrics and the inertia parameter to determine whether to transfer responsibility for a compute process on a first storage server node to a second storage server node” [Longo ¶ 80]. Accordingly, different statistical values (e.g., a min, max, standard deviation, average, etc.) can be calculated for each metric. One or more of the metrics can be used to calculate a value that represents one or more compute load parameters of the storage system” [Longo ¶ 66].
wherein the vendor environment database maintains a data protection landscape; “Different volume servers 322 may be responsible for different volumes. In this case, redirector server 320 is used to redirect the client to a specific volume server 322. To client 308, redirector server 320 may represent a single point of contact. The first request from client 308a then is redirected to a specific volume server 322. For example, redirector server 320 may use a database of volumes to determine which volume server 322 is a primary volume server for the requested target name” [Longo ¶ 57]. “In the system 300 depicted in FIG. 3, the client 308a utilizes storage services of a volume 330a, a client 308b utilizes storage services of a volume 330b, and a client 308n utilizes storage services of a volume 330n. In some embodiments, the volumes may exist in multiple different protection domains” [Longo ¶ 68].
making a determination that the resource utilization value of the first resource exceeds a predetermined maximum resource utilization level, wherein a user of the first CEDPM utilizes the first resource on the first CEDPM; “In some embodiments, the inertial parameter(s) may be generated by applying one or more weighting factor(s) to the parameters determined in operation 415 and may be generated for one or more of the parameters collected and may be used to adjust the relative weight assigned to the parameters in subsequent processing … In some embodiments, the inertial parameter may be as part of a cost/benefit calculation to determine whether the costs of moving processes from a particular volume outweigh the potential benefits of moving the processes from the particular volume … At block 425, it is determined whether the inertial parameter(s) for the respective volume(s) generated in block 420 are less than a threshold” [Longo ¶ 76-78, Fig. 4 Examiner notes that at block 425 if the outcome is yes then this indicates that the inertial parameter(s) exceeds the maximum]. “If, at block 425, the inertial parameter for a given volume is not less than the threshold then control passes back to block 410 and the process 400 continues to collect metrics for that volume. By contrast, if at operation 425 the inertial parameter for a given volume is less than the threshold then control passes to block 430 and a performance capacity of one or more protection domains in the system is determined” [Longo ¶ 79 Examiner notes the threshold is considered a maximum for the inertial parameter which is in part based on the resource utilization level].
generating, based on the determination, a recommendation to modify the resource utilization value of the first resource; “If, at operation 435, a protection domain does not have sufficient performance capacity to accept additional volumes, then control passes back to block 410 and the process 400 continues to collect metrics. By contrast, if at operation 435 a protection domain has sufficient performance capacity to accept additional volumes, then control passes to operation 440 and a load balancing algorithm is applied to rebalance compute load between available protection domains. In some examples an instruction may be generated that causes a processor to use the real-time performance metrics and the inertia parameter to determine whether to transfer responsibility for a compute process on a first storage server node to a second storage server node” [Longo ¶ 80].
wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization across a plurality of client environment data protection modules; “FIG. 4 is a flow diagram illustrating operations in a technique to implement dynamic load balancing by analyzing performance of volume to quality of service (QoS) in accordance with an embodiment of the present disclosure” [Longo ¶ 14]. “By contrast, if at operation 435 a protection domain has sufficient performance capacity to accept additional volumes, then control passes to operation 440 and a load balancing algorithm is applied to rebalance compute load between available protection domains” [Longo ¶ 79].
wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, “As noted above, in some embodiments, a performance manager module (e.g., performance manager 138 shown in FIG. 1) may poll an API (e.g., API 137 shown in FIG. 1) of a distributed storage system (e.g., cluster 135 shown in FIG. 1) of which the storage node 200 is a part to obtain various telemetry data of the distributed storage system. The telemetry data may represent performance metrics (utilization performance), configuration and other system data associated with various levels or layers of the cluster or the storage node 200. For example, metrics may be available for individual or groups of storage nodes (e.g., 136a-n), individual or groups of volumes 221, individual or groups of slice services 220, and/or individual or groups of block services 215” [Longo ¶ 43]. “In various embodiments described herein, an administrator (e.g., user 112) of a distributed storage system (e.g., cluster 135) or a managed service provider responsible for multiple distributed storage systems of the same or multiple customers may monitor various telemetry data of the distributed storage system or multiple distributed storage systems via a browser-based interface presented on computer system 110” [Longo ¶ 29]. “Performance manager 138 can be configured to periodically poll and/or monitor for compute load parameters of the cluster 135 via the API 137. In some examples the polling may be performed on static periodic intervals. In other examples the polling interval may vary based upon one or more parameters (e.g., load, capacity, etc.). Depending upon the particular implementation, the polling may be performed at a predetermined or configurable interval (e.g., X milliseconds or Y seconds).” [Longo ¶ 34].
Longo fails to teach wherein the resource utilization value is obtained by employing a first set of machine learning (ML) models; identifying, based on the CM and for the first CEDPM and the second CEDPM, a plurality of modifications that have been performed on each identified resource over the DPOT; generating a holistic profile for each CEDPM based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; storing the holistic profile of each CEDPM in a vendor environment database, making a determination that the resource utilization value of the first resource exceeds a predetermined maximum resource utilization level, generating, based on the determination, a recommendation to modify the resource utilization value of the first resource, wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization and sending the recommendation and at least a portion of the holistic profile of the first CEDPM to the user, wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, initiating a recommendation monitoring service to track whether the recommendation is implemented by the user, and implementing, by the user, the recommendation.
However, Featonby teaches:
wherein the resource utilization value is obtained by employing a first set of machine learning (ML) models; “In this way, the ML component 222 may generate resource-utilization models 224 (first set of ML models) for each workload category 220 using anonymized historical-utilization data 412 for workloads 406 hosted by VM instances 404 across the computing-resource network 110. The resource-utilization models 224 may be representative of resource-utilization data 138 for the workloads 406 that are included in the workload categories 220” [Featonby Col. 23 Lines 5-12]. “At 704, the optimization service 106 may receive utilization data 138 indicating a resource-utilization characteristic of the workload 136 during execution. The resource-utilization characteristic may indicate at least one of an amount of the computing resources consumed by the workload 126 or a type of the computing resources consumed by the workload 136” [Featonby Col. 27 Lines 47-53]. “The resource-utilization models 224 may be representative of resource-utilization data 138 for the workloads 406 that are included in the workload categories 220. In this way, when a new workload 136 needs to be categorized for purposes of identifying optimized VM instance types 130, the resource-utilization data 138 for the new workload 136 may be mapped to the resource-utilization model 224 that is "closest" or "most near" the fingerprint of the resource-utilization data 138 for the new workload 136” [Featonby Col. 23 Lines 9-18].
identifying, based on the CM and for the first CEDPM and the second CEDPM, a plurality of modifications that have been performed on each identified resource over the DPOT; “For example, the workload may have changed over time (e.g., software update, new features, increase in user traffic, etc.) that in tum results in different resource consumption characteristics of the workload. In light of such modifications or changes, the optimization service may continually, or periodically, analyze the resource-utilization characteristics of the workload and determine if resource consumption has changed significantly enough such that a new VM instance type is more appropriate for the workload than the current VM instance type” [Featonby Col. 6 Lines 10-20]. “For instance, the user 105 may provide an indication to the optimization service 106 that the workload 136 has undergone a configuration change (e.g., update, software change, traffic change, etc.) that will likely result in a change in the resource-utilization characteristics of the workload 136” [Featonby Col. 14-15 Lines 65-67, 1-3].
generating a holistic profile for each CEDPM “The computer-readable media 206 may further store a profile generator 216 that generates a snapshot of profiling data, such as a resource-utilization characteristic included in the resource-utilization data 138, at regular intervals” [Featonby Col. 16 Lines 48-51]. “These fingerprints or profiles may be included in the resource-utilization data 138 and be mapped to VM instance types 130 and/or workload categories for the workload 136” [Featonby Col. 16 Lines -55-58].
based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; “In some examples, the optimization service 106 may further monitor the workload 136 for the life of the workload 136, and provide additional recommendation data 132 upon detecting events that result in a different VM instance type 130 being more optimized to support the workload than the current VM instance type 130 to which the VM instance 114 corresponds. For instance, the user 105 may provide an indication to the optimization service 106 that the workload 136 has undergone a configuration change (e.g., update, software change, traffic change, etc.) that will likely result in a change in the resource-utilization characteristics of the workload 136” [Featonby Col. 14-15 Lines 59-67, 1-3].
storing the holistic profile of each CEDPM in a vendor environment database, “Additionally, the service provider network 102 may include a data store 208 which may comprise one, or multiple, repositories or other storage locations for persistently storing and managing collections of data such as databases, simple files, binary, and/or any other data. The data store 208 may include one or more storage locations that may be managed by one or more database management systems” [Featonby Col. 16 Lines 16-23, Fig. 2 Examiner notes the location of Workload Categories 220 within Data Store 208 in figure 2].
making a determination that the resource utilization value of the first resource exceeds a predetermined maximum resource utilization level, “At 1806, the service provider network 102 may determine that the actual utilization rate is different than a desired utilization rate specified for the user account” [Featonby Col. 38 Lines 10-12]. “To determine performance 1722, the optimization service 106 may compare throughput of data, such as overall utilization 1720) for the respective compute type (e.g., CPU, memory, disk, GPU, network throughput, etc.), versus the baseline and/or across the different chipset models. In this way, the optimization service 106 may determine how performant one chipset model is compared to another chipset model. Although illustrated as being CPU usage, the performance 1722 may be determined for one or more of the dimensions of compute (e.g., CPU, memory, disk, GPU, and network throughput) for the different chipset models and/or device IDs 1714 … As a specific example, a user account 242 may be hosting their workload 1706 on a first VM instance type 130(1) that is supported by a first chipset model 1714. However, that workload 1706 may be consuming too much CPU (and/or other computing resource) compared to a utilization goal or preference” [Featonby Col. 36-37 Lines 64-67, 1-9, 28-33].
generating, based on the determination, a recommendation to modify the resource utilization value of the first resource, wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization “The workload categories may further be associated with the VM instance types that are optimized for the resource-utilization characteristics of the represented workloads. In this way, when resource-utilization characteristics are obtained from a description provided by a new user, or through actual utilization data throughout the life of a workload, the resource-utilization characteristics may be mapped to the "closest" resource-utilization model of a predefined workload category, and the associated VM instance types for that workload category may be provided as recommendations to optimize performance of the workload” [Featonby Col. 7 Lines 12-23]. “In this way, performance metrics may be assigned to the underlying computing device on which a VM instance is provisioned to help determine an optimized VM instance type based on the computing device that is to be utilized. In an example where a workload is migrated from a less compute-performant device onto a more compute-performant device, the optimization service may select a new VM instance type based in part on a ratio of the performance between the two devices. In this way, the optimization service may select a new VM instance type that may not need be allocated as much compute power of the more compute-performance computing device” [Featonby Col. 7 Lines 49-60].
and sending the recommendation “FIG. 11 illustrates a flow diagram of an example method for determining that a new VM instance type is more optimized to support a workload than a current VM instance type, recommending the new VM instance type to a user account associated with the workload, and migrating the workload to the new VM instance type” [Featonby Col. 2 Lines 44-49].
and at least a portion of the holistic profile of the first CEDPM to the user. “As shown, the GUI 502 may present instance type 504, suitability 506, and explanations 508 for the recommended VM instance types 130. In the illustrated example, a first VM instance 510 may be storage optimized, have a suitability 506 of 4.5 out of 5 stars, and have an explanation 508 indicating that the VM instance type 510 delivers additional storage with sufficient compute for the workload 130” [Featonby Col. 24 Lines 37-43].
wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, “The GUI 300 may be presented on a user device 108, and accessible via a user account 242 and a console 122. In some examples, the GUI 300 may be part of the web-console wizard 212 that assists the user 105 in selecting an optimized or appropriate VM instance type 130 for a new workload 136” [Featonby Col. 19 Lines 62-67]. “The GUI 502 may comprise options through which a user 105 can select or choose a VM instance type 130. The GUI 502 may list different VM instances types 130 that have been determined by the optimization service 106 as being optimized for the workload 130 associated with the user account 242 through which the console 122 is accessed” [Featonby Col. 24 Lines 28-33]. “As a specific example, a user account 242 may be hosting their workload 1706 on a first VM instance type 130(1) that is supported by a first chipset model 1714. However, that 30 workload 1706 may be consuming too much CPU (and/or other computing resource) compared to a utilization goal or preference. The optimization service 106 may determine that a second VM instance type 130(2) that is supported by a second chipset model 1714 is more appropriate to host the workload 136 to achieve the utilization goal because, even if the second chipset model and second VM instance type 130(2) have less CPUs 1718 and/or vCPUs 1716, the ratio of performance metrics 1724 between the first and second chipset models 1714 may indicate that the second chipset model 1714 will still achieve lower utilization, and the same or higher throughput, to support the workload 1706. The optimization service 106 may select VM instance types 130 based at least in part on the performance metrics 1724 for the underlying computing resources of the supporting devices” [Featonby Col. 37 Lines 28-45].
initiating a recommendation monitoring service to track whether the recommendation is implemented by the user, and implementing, by the user, the recommendation, “Using this recommendation data 132, the user 105 can make a more informed decision as to what VM instance type 130 to utilize to support their workload 130, check a box next to the VM instance type 130 they desire, and further provide input into a select instance type control 516. Upon selecting the instance type, selection data 518 may be sent from the user device 108, over the network(s) 118, to the service provider network 102 (recommendation monitoring service) to indicate that the user 105 is requesting to have their workload 130 launched or supported by the first VM instance type 510” [Featonby Col. 24 Lines 53-62]. “The GUI 502 may allow the user 102 to select one of the VM instance types 130 to launch their workload 130 in an automated fashion” [Featonby Col. 24 Lines 34-36].
Longo teaches a method of metadata monitoring where resource utilization data is computed and averaged then included in a set of resource parameters used in making optimal decisions for protection domain management. This can be combined with the teachings of Featonby to generate a profile with the resource utilization data and to provide the optimal decisions as suggestions to users. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo to incorporate the teachings of Featonby and include wherein the resource utilization value is obtained by employing a first set of machine learning (ML) models; identifying, based on the CM and for the first CEDPM and the second CEDPM, a plurality of modifications that have been performed on each identified resource over the DPOT; generating a holistic profile for each CEDPM based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; storing the holistic profile of each CEDPM in a vendor environment database, making a determination that the resource utilization value of the first resource exceeds a predetermined maximum resource utilization level, generating, based on the determination, a recommendation to modify the resource utilization value of the first resource, wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization and sending the recommendation and at least a portion of the holistic profile of the first CEDPM to the user, wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, initiating a recommendation monitoring service to track whether the recommendation is implemented by the user, and implementing, by the user, the recommendation. Doing so would allow for decisions made for optimization to be provided as recommendations to users before they are implemented. “In this way, the optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network” [Featonby Col. 5 Lines 27-31].
Longo in view of Featonby fails to teach wherein the holistic profile for each CEDPM is generated by employing a second set of ML models.
However, Shetty teaches wherein the holistic profile for each CEDPM is generated by employing a second set of ML models; “In another example, if a new device is obtained by an organization, the hardware specs of the device can be inputted into the first ML model to generate a device profile. The device profile can be inputted into the third ML model to identify users or user groups to whom the new device is recommended” [Shetty ¶ 101].
Shetty is considered to be analogous to the claimed invention because it is in the same field of allocation of resources. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby to incorporate the teachings of Shetty and include wherein the holistic profile for each CEDPM is generated by employing a second set of ML models. Doing so would allow for the consideration of further CEDPM characteristics within the evaluations. “A second ML model can be trained to learn how a device is used by a user or users with similar roles. The second ML model can be trained using data related to the average lifespan of a device before replacement, software capabilities (maximum OS upgrade, support for certain apps, and so on), and general use cases of a device (e.g., the types of user to whom a particular device is assigned)” [Shetty ¶ 101].
Longo in view of Featonby in view of Shetty fails to explicitly teach wherein upon implementation, making the first CEDPM more user-friendly by reducing a number of steps required for subsequent operations.
However, Adam teaches wherein upon implementation, making the first CEDPM more user-friendly by reducing a number of steps required for subsequent operations. “And in some cases the system may automatically do certain actions that may no longer need explicit user interaction or automate them into a single step, especially when the prediction system for a particular user achieves a high level of reliability” [Adam ¶ 46]. “In step 806, user experience system 104 automates the next action. In automating the system is not presenting the action on the adapted user interface. Instead, it is performing the action for the user and then moving to the next predicted action and adapted user interface. Because of the decision in step 804, these actions are automated without risk for the software to perform an action not wanted by the user” [Adam ¶ 108].
Adam is considered to be analogous to the claimed invention because it is in the same field of interaction techniques based on graphical user interfaces. Longo in view of Featonby in view of Shetty presents a system wherein recommendations pertaining to CEPDMs are implemented by users. The teachings of Adam can be combined with this system to reduce a number of steps required for subsequent operations after the recommendation of Longo in view of Featonby in view of Shetty is implemented. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Shetty to incorporate the teachings of Adam and include wherein upon implementation, making the first CEDPM more user-friendly by reducing a number of steps required for subsequent operations. Doing so would allow for further ease of user for the user. “A less experienced user may not care to know the advanced features of a software application and may be using the application a few times to complete a straight forward task. Thus, the it is sometimes valuable to provide a simpler adapted user interface in some scenarios” [Adam ¶ 83].
With regard to claim 17, Longo in view of Featonby in view of Shetty in view of Adam teaches the non-transitory computer-readable medium of claim 15, as referenced above. Longo further teaches:
wherein the first resource is a virtual machine, “As an example, a computer may be one or more server computers, cloud-based computers, cloud-based cluster of computers, virtual machine instances or virtual machine computing elements such as virtual processors, storage and memory, data centers, storage devices, desktop computers, laptop computers, mobile devices, or any other special-purpose computing devices” [Longo ¶ 22]. “Entities in system 300 may be virtualized entities. For example, multiple virtual block servers 312 may be included on a machine. Entities may also be included in a cluster, where computing resources of the cluster are virtualized such that the computing resources appear as a single entity” [Longo ¶ 55, Fig. 3C Examiner notes the volumes 330a-n in figure 3C].
wherein the virtual machine provides at least one computer-implemented service to the user. “Each slice service 220 may include one or more volumes (e.g., volumes 22la-x, volumes 22lc-y, and volumes 22le-z). Client systems (not shown) associated with an enterprise may store data to one or more volumes, retrieve data from one or more volumes, and/or modify data stored on one or more volumes” [Longo ¶ 38].
With regard to claim 19, Longo in view of Featonby in view of Shetty in view of Adam teaches the non-transitory computer-readable medium of claim 15, as referenced above. Longo further teaches wherein the product configuration information specifies at least one selected from a group consisting of a type of each of a plurality of assets, a number of each type of the plurality of assets, a size of each of the plurality of assets, a type of an operating system, and a number of each type of a plurality of data protection policies. “In some examples a cloud-based QoS system may collect, from a plurality of the volumes 330a, 330b, 330n, on a per-volume basis, one or more real-time performance metrics for one or more compute processes executing on the one or more computer systems” [Longo ¶ 69]. “At block 415, at least one of a volume size, a volume activity level, or a number of client sessions operating on a volume is determined” [Longo ¶ 74].
Claims 2, 6, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Longo (US 2022/0276907 A1) in view of Featonby (US 11,360,795 B2) in view of Shetty (US 2023/0396487 A1) in view of Adam (US 2018/0364879 A1) in view of Shats (US 11,036,594 B1).
With regard to claim 2, Longo in view of Featonby in view of Shetty in view of Adam teaches the method of claim 1, as referenced above. Longo in view of Featonby in view of Shetty in view of Adam fails to teach wherein the first resource is a cloud disaster recovery policy, wherein the cloud disaster recovery policy is configured to recover a network-attached storage device that is utilized by the user.
However, Shats teaches wherein the first resource is a cloud disaster recovery policy, wherein the cloud disaster recovery policy is configured to recover a network-attached storage device that is utilized by the user. “Although it should be understood that in certain embodiments, a single protected site 150 may be in direct communication with an object store 100 of a data recovery system (e.g., via one or more IO filters), various embodiments may utilize one or more recovery groups and/or disaster recovery virtual appliances to facilitate efficient storage of data within the object storage 150” [Shats Col. 13 Lines 23-29, Fig. 5]. “Moreover, each virtual machine 151 has an IO filter 153 for intercepting data as it is written to local primary storage 152 during typical operation of the virtual machine 151 and for continuously replicating the data through a protection domain to the object store 100” [Shats Col. 13 Lines 35-40]. “In certain embodiments, the protected site 150 may utilize any datastore type compatible with the virtualization platform, including but not limited to Storage Area Network (SAN), Network Attached Storage (NAS) or server-integrated storage commonly referred to as hyper-converged infrastructure (HCI)” [Shats Col. 14 Lines 5-11].
Shats is considered to be analogous to the claimed invention because it is in the same field of hypervisor-specific management and integration aspects. The cloud disaster recovery methods of Shats which operate through protection domains can be combined with the protection domains of Longo. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Shetty in view of Adam to incorporate the teachings of Shats and include that the first resource is a cloud disaster recovery policy, wherein the cloud disaster recovery policy is configured to recover a network-attached storage device that is utilized by the user. Doing so would allow for a fast system recovery when encountering failures of different magnitudes. “Such embodiments provide a method for maintaining business continuity by resuming operations as quickly as possible (e.g., with minimal data interruption) upon detection of a failure of the protected site 150 (as reflected at Block 2002), even if resuming operations at the original protected site 150 is not possible for some period of time, such as during a fire, flood or other natural disaster or a severe hardware failure” [Shats Col. 9 Lines 11-18].
With regard to claim 6, Longo in view of Featonby in view of Shetty in view of Adam teaches the method of claim 1, as referenced above. Longo further teaches wherein the recommendation specifies obtaining a third CEDPM “The performance metrics may be used to perform load balancing operations by shifting volume compute responsibilities (i.e., read/write, hashing, compression) between storage services and/or protection domains” [Longo ¶ 16]. “In some embodiments, the volumes may exist in multiple different protection domains (CEDPMs). For example, volume 330a may exist in a first protection domain, volume 330b may exist in a second protection domain, and volume 330n may exist in a third protection domain” [Longo ¶ 68].
Longo fails to teach wherein the recommendation specifies obtaining a third CEDPM from a vendor.
However, Featonby teaches wherein the recommendation specifies obtaining a third CEDPM from a vendor “In other examples, the service provider network may develop and offer new VM instance type(s) to increase the offerings of VM instance types for users. The optimization service may use various techniques, such as workload simulation, to determine that the new VM instance type is more optimized for the workload (or workload category to which the workload belongs) than the currently utilized VM instance type. For such reasons, and potentially other reasons, the optimization service may provide the user account with recommendations that the user migrate their workload from the current VM instance type to be hosted by a different VM instance type that is more optimized for the resource consumption/utilization of the workload” [Featonby Col. 6 Lines 20-33].
Longo in view of Featonby in view of Shetty in view of Adam fails to teach in order to reduce a number of a plurality of virtual machines protected by the first CEDPM, wherein the first CEDPM is provided to the user by the vendor.
However, Shats teaches in order to reduce a number of a plurality of virtual machines protected by the first CEDPM, wherein the first CEDPM is provided to the user by the vendor. “A protection domain 156 can include multiple recovery groups 155, and in certain embodiments recovery groups 155 can be moved from one protection domain 156 to another, for example, for load balancing” [Shats Col. 14 Lines 45-49]. “In certain embodiments, multiple logically related systems (e.g., multiple virtual machines 151) may be assigned to a recovery group 155 …” [Shats Col. 14 Lines 21-23].
With regard to claim 16, Longo in view of Featonby in view of Shetty in view of Adam teaches the non-transitory computer-readable medium of claim 15, as referenced above. Longo in view of Featonby in view of Shetty in view of Adam fails to teach wherein the first resource is a cloud disaster recovery policy, wherein the cloud disaster recovery policy is configured to recover a network-attached storage device that is utilized by the user.
However, Shats teaches wherein the first resource is a cloud disaster recovery policy, wherein the cloud disaster recovery policy is configured to recover a network-attached storage device that is utilized by the user. “Although it should be understood that in certain embodiments, a single protected site 150 may be in direct communication with an object store 100 of a data recovery system (e.g., via one or more IO filters), various embodiments may utilize one or more recovery groups and/or disaster recovery virtual appliances to facilitate efficient storage of data within the object storage 150” [Shats Col. 13 Lines 23-29, Fig. 5]. “Moreover, each virtual machine 151 has an IO filter 153 for intercepting data as it is written to local primary storage 152 during typical operation of the virtual machine 151 and for continuously replicating the data through a protection domain to the object store 100” [Shats Col. 13 Lines 35-40]. “In certain embodiments, the protected site 150 may utilize any datastore type compatible with the virtualization platform, including but not limited to Storage Area Network (SAN), Network Attached Storage (NAS) or server-integrated storage commonly referred to as hyper-converged infrastructure (HCI)” [Shats Col. 14 Lines 5-11].
The cloud disaster recovery methods of Shats which operate through protection domains can be combined with the protection domains of Longo. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Shetty in view of Adam to incorporate the teachings of Shats and include that the first resource is a cloud disaster recovery policy, wherein the cloud disaster recovery policy is configured to recover a network-attached storage device that is utilized by the user. Doing so would allow for a fast system recovery when encountering failures of different magnitudes. “Such embodiments provide a method for maintaining business continuity by resuming operations as quickly as possible (e.g., with minimal data interruption) upon detection of a failure of the protected site 150 (as reflected at Block 2002), even if resuming operations at the original protected site 150 is not possible for some period of time, such as during a fire, flood or other natural disaster or a severe hardware failure” [Shats Col. 9 Lines 11-18].
With regard to claim 20, Longo in view of Featonby in view of Shetty in view of Adam teaches the non-transitory computer-readable medium of claim 15, as referenced above. Longo further teaches wherein the recommendation specifies obtaining a third CEDPM “The performance metrics may be used to perform load balancing operations by shifting volume compute responsibilities (i.e., read/write, hashing, compression) between storage services and/or protection domains” [Longo ¶ 16]. “In some embodiments, the volumes may exist in multiple different protection domains (CEDPMs). For example, volume 330a may exist in a first protection domain, volume 330b may exist in a second protection domain, and volume 330n may exist in a third protection domain” [Longo ¶ 68].
Longo fails to teach wherein the recommendation specifies obtaining a third CEDPM from a vendor.
However, Featonby teaches wherein the recommendation specifies obtaining a third CEDPM from a vendor “In other examples, the service provider network may develop and offer new VM instance type(s) to increase the offerings of VM instance types for users. The optimization service may use various techniques, such as workload simulation, to determine that the new VM instance type is more optimized for the workload (or workload category to which the workload belongs) than the currently utilized VM instance type. For such reasons, and potentially other reasons, the optimization service may provide the user account with recommendations that the user migrate their workload from the current VM instance type to be hosted by a different VM instance type that is more optimized for the resource consumption/utilization of the workload” [Featonby Col. 6 Lines 20-33].
Longo in view of Featonby in view of Shetty in view of Adam fails to teach in order to reduce a number of a plurality of virtual machines protected by the first CEDPM, wherein the first CEDPM is provided to the user by the vendor.
However, Shats teaches in order to reduce a number of a plurality of virtual machines protected by the first CEDPM, wherein the first CEDPM is provided to the user by the vendor. “A protection domain 156 can include multiple recovery groups 155, and in certain embodiments recovery groups 155 can be moved from one protection domain 156 to another, for example, for load balancing” [Shats Col. 14 Lines 45-49]. “In certain embodiments, multiple logically related systems (e.g., multiple virtual machines 151) may be assigned to a recovery group 155 …” [Shats Col. 14 Lines 21-23].
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Longo (US 2022/0276907 A1) in view of Featonby (US 11,360,795 B2) in view of Shetty (US 2023/0396487 A1) in view of Adam (US 2018/0364879 A1) in view of Venkatesh (US 2018/0157561 A1).
With regard to claim 4, Longo in view of Featonby in view of Shetty in view of Adam teaches the method of claim 1, as referenced above. Longo in view of Featonby in view of Shetty in view of Adam fails to teach wherein the first resource is a centralized protection policy, wherein the centralized protection policy is configured to protect a virtual machine utilized by the user.
However, Venkatesh teaches:
wherein the first resource is a centralized “In particular embodiments, failure of FSVMs 170 may be detected using the centralized coordination service” [Venkatesh ¶ 93].
protection policy, wherein the centralized protection policy is configured to protect a virtual machine utilized by the user. “Particular embodiments enable creation of protection domains comprising one or more consistency groups of VMs, volume groups, or other elements. Such protection domains may be scheduled for snapshots at regular intervals (e.g., every 12 hours) for disaster recovery or other purposes” [Venkatesh ¶ 61]. “A custom replication policy may be configured for the VFS 202, and the ability may be provided to map the VFS 202's configuration between sites to provide disaster recovery of virtual file-services across geographical locations. Particular embodiments may provide the ability to protect individual shares or share groups by protecting the volume group(s) used for file-services storage, e.g., by adding them to a protection domain” [Venkatesh ¶ 146].
Venkatesh is considered to be analogous to the claimed invention because it is in the same field of protected arrangements for virtualization. The centralized protection policy of Venkatesh to protect volumes within a protection domain can be combined with the protection domains of Longo. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Shetty in view of Adam to incorporate the teachings of Venkatesh and include that the first resource is a centralized protection policy, wherein the centralized protection policy is configured to protect a virtual machine utilized by the user. Doing so would allow for service recovery across geographical locations. “A custom replication policy may be configured for the VFS 202, and the ability may be provided to map the VFS 202's configuration between sites to provide disaster recovery of virtual file-services across geographical locations” [Venkatesh ¶ 146].
With regard to claim 18, Longo in view of Featonby in view of Shetty in view of Adam teaches the non-transitory computer-readable medium of claim 15, as referenced above. Longo in view of Featonby in view of Shetty in view of Adam fails to teach wherein the first resource is a centralized protection policy, wherein the centralized protection policy is configured to protect a virtual machine utilized by the user.
However, Venkatesh teaches:
wherein the first resource is a centralized “In particular embodiments, failure of FSVMs 170 may be detected using the centralized coordination service” [Venkatesh ¶ 93].
protection policy, wherein the centralized protection policy is configured to protect a virtual machine utilized by the user. “Particular embodiments enable creation of protection domains comprising one or more consistency groups of VMs, volume groups, or other elements. Such protection domains may be scheduled for snapshots at regular intervals (e.g., every 12 hours) for disaster recovery or other purposes” [Venkatesh ¶ 61]. “A custom replication policy may be configured for the VFS 202, and the ability may be provided to map the VFS 202's configuration between sites to provide disaster recovery of virtual file-services across geographical locations. Particular embodiments may provide the ability to protect individual shares or share groups by protecting the volume group(s) used for file-services storage, e.g., by adding them to a protection domain” [Venkatesh ¶ 146].
The centralized protection policy of Venkatesh to protect volumes within a protection domain can be combined with the protection domains of Longo. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Shetty in view of Adam to incorporate the teachings of Venkatesh and include that the first resource is a centralized protection policy, wherein the centralized protection policy is configured to protect a virtual machine utilized by the user. Doing so would allow for service recovery across geographical locations. “A custom replication policy may be configured for the VFS 202, and the ability may be provided to map the VFS 202's configuration between sites to provide disaster recovery of virtual file-services across geographical locations” [Venkatesh ¶ 146].
Claims 8, 10, 12, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Longo (US 2022/0276907 A1) in view of Featonby (US 11,360,795 B2) in view of Wu (US 2024/0031429 A1) in view of Shetty (US 2023/0396487 A1) in view of Adam (US 2018/0364879 A1).
With regard to claim 8, Longo teaches:
A method for managing a data protection module, the method comprising: obtaining client metadata (CM) “The slice services 220a-n may store metadata that maps between client systems and block services 215” [Longo ¶ 39]. “A data block, therefore, is the raw data for a volume and may be the smallest addressable unit of data. The metadata layer 304 or the client layer 302 can break data into data blocks. The data blocks can then be stored on multiple block servers 312a-n” [Longo ¶ 49].
of a first client environment data protection module (CEDPM) and a second CEDPM, “In some embodiments, the volumes may exist in multiple different protection domains (CEDPMs). For example, volume 330a may exist in a first protection domain, volume 330b may exist in a second protection domain, and volume 330n may exist in a third protection domain” [Longo ¶ 68]. “As used herein, a "protection domain" refers to a single storage server node (CEDPM) in a storage system” [Longo ¶ 79].
wherein the CM comprises at least product configuration information; “Depending upon the particular embodiment, the API 137 may provide access to various telemetry data (e.g., performance, configuration and other system data) relating to the cluster 135 or components thereof” [Longo ¶ 33]. “Metadata layer 304 includes one or more metadata servers 310a-n. Performance managers 314a-n may be located on metadata servers 310a-n” [Longo ¶ 47]. “noted above, in some embodiments, a performance manager module (e.g., performance manager 138 shown in FIG. 1) may poll an API (e.g., API 137 shown in FIG. 1) of a distributed storage system (e.g., cluster 135 shown in FIG. 1) of which the storage node 200 is a part to obtain various telemetry data of the distributed storage system” [Longo ¶ 43].
identifying, based on the CM and for the first CEDPM and the second CEDPM, a first resource and a second resource that have been utilized over a defined period of time (DPOT); “The method comprises collecting, from a plurality of volumes (resources) on a per-volume basis, one or more real-time performance metrics for one or more compute processes executing on the one or more computer systems…” [Longo ¶ 4]. “In some embodiments, metrics may be collected on a periodic basis. The period may be static or may vary with operational conditions. In some embodiments the collection frequency is configurable, but defaults to every 500 ms. Collection happens simultaneously on each node (CEDPM) of a storage system, and occurs within the slice service of each storage node” [Longo ¶ 72]. “As used herein, a "protection domain" refers to a single storage server node in a storage system” [Longo ¶ 79 Examiner notes both the protection domains and the storage nodes they refer to are considered one CEDPM]. “The performance manager 138 may locally process and/or aggregate the collected compute load parameters (e.g., latency, utilization, IOPS, SS load, Quality of Service (QoS) settings, etc.) over a period of time by data point values and/or by ranges of data point values and provide frequency information regarding the aggregated compute load parameters retrieved from the cluster 135 to the normalizing agent 230” [Longo ¶ 35].
obtaining, based on the CM and for the first CEDPM and the second CEDPM, a resource utilization value for each identified resource over the DPOT; “The performance manager 138 may locally process and/or aggregate the collected compute load parameters (e.g., latency, utilization, IOPS, SS load, Quality of Service (QoS) settings, etc.) over a period of time by data point values and/or by ranges of data point values and provide frequency information regarding the aggregated compute load parameters retrieved from the cluster 135 to the normalizing agent 230” [Longo ¶ 35]. “Further, in some examples the one or more real-time performance metrics comprises at least one of a percentage of processor utilization metric, an input/ output (I/O) throughput metric, an average I/O size metric, an average read latency, or an average write latency” [Longo ¶ 69].
deriving, based on the resource utilization value for each identified resource, a first average resource utilization value for the first resource and a second average resource utilization value for the second resource; “Metrics, including system metrics, background process metrics, and client metrics, can be calculated over a period of time (e.g., 250 ms, 500 ms, 1 s, etc). Accordingly, different statistical values (e.g., a min, max, standard deviation, average, etc.) can be calculated for each metric. One or more of the metrics can be used to calculate a value that represents one or more compute load parameters of the storage system” [Longo ¶ 66]. “In some examples an exponential moving average (EMA) may be determined across the performance metrics. For example, a 30% EMA may be used such that recent samples of performance are only weighted at 30% versus all past historic samples being weighted at 70%” [Longo ¶ 76]. “The method comprises collecting, from a plurality of volumes (resources) on a per-volume basis, one or more real-time performance metrics for one or more compute processes executing on the one or more computer systems…” [Longo ¶ 4 Examiner notes that the performance metrics and parameters of Longo are collected for each volume and thus this is considered to mean that there is a first average resource utilization value for the first resource and a second average resource utilization value for the second resource].
generating a holistic (set of parameters) profile for each CEDPM “The performance manager 138 may locally process and/or aggregate the collected compute load parameters (e.g., latency, utilization, IOPS, SS load, Quality of Service (QoS) settings, etc.) over a period of time by data point values and/or by ranges of data point values and provide frequency information regarding the aggregated compute load parameters retrieved from the cluster 135 to the normalizing agent 230” [Longo ¶ 34].“As those skilled in the art will appreciate various other types of telemetry data may be made available via the API 137, including, but not limited to measures of latency, utilization, and/or performance at various levels (e.g., the cluster level, the storage node level, or the storage node component level)” [Longo ¶ 33].
based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, the first average resource utilization value, and the second average resource utilization value; “In some examples an instruction may be generated that causes a processor to use the real-time performance metrics and the inertia parameter to determine whether to transfer responsibility for a compute process on a first storage server node to a second storage server node” [Longo ¶ 80]. Accordingly, different statistical values (e.g., a min, max, standard deviation, average, etc.) can be calculated for each metric. One or more of the metrics can be used to calculate a value that represents one or more compute load parameters of the storage system” [Longo ¶ 66].
wherein the vendor environment database maintains a data protection landscape; “Different volume servers 322 may be responsible for different volumes. In this case, redirector server 320 is used to redirect the client to a specific volume server 322. To client 308, redirector server 320 may represent a single point of contact. The first request from client 308a then is redirected to a specific volume server 322. For example, redirector server 320 may use a database of volumes to determine which volume server 322 is a primary volume server for the requested target name” [Longo ¶ 57]. “In the system 300 depicted in FIG. 3, the client 308a utilizes storage services of a volume 330a, a client 308b utilizes storage services of a volume 330b, and a client 308n utilizes storage services of a volume 330n. In some embodiments, the volumes may exist in multiple different protection domains” [Longo ¶ 68].
making a first determination that the resource utilization value of the first resource does not exceed a predetermined maximum resource utilization level; “In some embodiments, the inertial parameter(s) may be generated by applying one or more weighting factor(s) to the parameters determined in operation 415 and may be generated for one or more of the parameters collected and may be used to adjust the relative weight assigned to the parameters in subsequent processing … In some embodiments, the inertial parameter may be as part of a cost/benefit calculation to determine whether the costs of moving processes from a particular volume outweigh the potential benefits of moving the processes from the particular volume … At block 425, it is determined whether the inertial parameter(s) for the respective volume(s) generated in block 420 are less than a threshold” [Longo ¶ 76-78, Fig. 4 Examiner notes that at block 425 if the outcome is no then this indicates that the inertial parameter(s) does not exceed the maximum]. “If, at block 425, the inertial parameter for a given volume is not less than the threshold then control passes back to block 410 and the process 400 continues to collect metrics for that volume. By contrast, if at operation 425 the inertial parameter for a given volume is less than the threshold then control passes to block 430 and a performance capacity of one or more protection domains in the system is determined” [Longo ¶ 79 Examiner notes the threshold is considered a maximum for the inertial parameter which is in part based on the resource utilization level].
generating, based on the second determination, a recommendation to modify the resource utilization value of the first resource; “If, at operation 435, a protection domain does not have sufficient performance capacity to accept additional volumes, then control passes back to block 410 and the process 400 continues to collect metrics. By contrast, if at operation 435 a protection domain has sufficient performance capacity to accept additional volumes, then control passes to operation 440 and a load balancing algorithm is applied to rebalance compute load between available protection domains. In some examples an instruction may be generated that causes a processor to use the real-time performance metrics and the inertia parameter to determine whether to transfer responsibility for a compute process on a first storage server node to a second storage server node” [Longo ¶ 80].
wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization across a plurality of client environment data protection modules; “FIG. 4 is a flow diagram illustrating operations in a technique to implement dynamic load balancing by analyzing performance of volume to quality of service (QoS) in accordance with an embodiment of the present disclosure” [Longo ¶ 14]. “By contrast, if at operation 435 a protection domain has sufficient performance capacity to accept additional volumes, then control passes to operation 440 and a load balancing algorithm is applied to rebalance compute load between available protection domains” [Longo ¶ 79].
wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, “As noted above, in some embodiments, a performance manager module (e.g., performance manager 138 shown in FIG. 1) may poll an API (e.g., API 137 shown in FIG. 1) of a distributed storage system (e.g., cluster 135 shown in FIG. 1) of which the storage node 200 is a part to obtain various telemetry data of the distributed storage system. The telemetry data may represent performance metrics (utilization performance), configuration and other system data associated with various levels or layers of the cluster or the storage node 200. For example, metrics may be available for individual or groups of storage nodes (e.g., 136a-n), individual or groups of volumes 221, individual or groups of slice services 220, and/or individual or groups of block services 215” [Longo ¶ 43]. “In various embodiments described herein, an administrator (e.g., user 112) of a distributed storage system (e.g., cluster 135) or a managed service provider responsible for multiple distributed storage systems of the same or multiple customers may monitor various telemetry data of the distributed storage system or multiple distributed storage systems via a browser-based interface presented on computer system 110” [Longo ¶ 29]. “Performance manager 138 can be configured to periodically poll and/or monitor for compute load parameters of the cluster 135 via the API 137. In some examples the polling may be performed on static periodic intervals. In other examples the polling interval may vary based upon one or more parameters (e.g., load, capacity, etc.). Depending upon the particular implementation, the polling may be performed at a predetermined or configurable interval (e.g., X milliseconds or Y seconds).” [Longo ¶ 34].
Longo fails to teach wherein the resource utilization value is obtained by employing a first set of machine learning (ML) models; identifying, based on the CM and for the first CEDPM and the second CEDPM, a plurality of modifications that have been performed on each identified resource over the DPOT; generating a holistic profile for each CEDPM based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; storing the holistic profile of each CEDPM in a vendor environment database, making a first determination that the resource utilization value of the first resource does not exceed a predetermined maximum resource utilization level; generating, based on the second determination, a recommendation to modify the resource utilization value of the first resource, wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization and sending the recommendation and at least a portion of the holistic profile of the first CEDPM to a user of the first CEDPM, wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, initiating a recommendation monitoring service to track whether the recommendation is implemented by the user, and implementing, by the user, the recommendation.
However, Featonby teaches:
wherein the resource utilization value is obtained by employing a first set of machine learning (ML) models; “In this way, the ML component 222 may generate resource-utilization models 224 (first set of ML models) for each workload category 220 using anonymized historical-utilization data 412 for workloads 406 hosted by VM instances 404 across the computing-resource network 110. The resource-utilization models 224 may be representative of resource-utilization data 138 for the workloads 406 that are included in the workload categories 220” [Featonby Col. 23 Lines 5-12]. “At 704, the optimization service 106 may receive utilization data 138 indicating a resource-utilization characteristic of the workload 136 during execution. The resource-utilization characteristic may indicate at least one of an amount of the computing resources consumed by the workload 126 or a type of the computing resources consumed by the workload 136” [Featonby Col. 27 Lines 47-53]. “The resource-utilization models 224 may be representative of resource-utilization data 138 for the workloads 406 that are included in the workload categories 220. In this way, when a new workload 136 needs to be categorized for purposes of identifying optimized VM instance types 130, the resource-utilization data 138 for the new workload 136 may be mapped to the resource-utilization model 224 that is "closest" or "most near" the fingerprint of the resource-utilization data 138 for the new workload 136” [Featonby Col. 23 Lines 9-18].
identifying, based on the CM and for the first CEDPM and the second CEDPM, a plurality of modifications that have been performed on each identified resource over the DPOT; “For example, the workload may have changed over time (e.g., software update, new features, increase in user traffic, etc.) that in tum results in different resource consumption characteristics of the workload. In light of such modifications or changes, the optimization service may continually, or periodically, analyze the resource-utilization characteristics of the workload and determine if resource consumption has changed significantly enough such that a new VM instance type is more appropriate for the workload than the current VM instance type” [Featonby Col. 6 Lines 10-20]. “For instance, the user 105 may provide an indication to the optimization service 106 that the workload 136 has undergone a configuration change (e.g., update, software change, traffic change, etc.) that will likely result in a change in the resource-utilization characteristics of the workload 136” [Featonby Col. 14-15 Lines 65-67, 1-3].
generating a holistic profile for each CEDPM “The computer-readable media 206 may further store a profile generator 216 that generates a snapshot of profiling data, such as a resource-utilization characteristic included in the resource-utilization data 138, at regular intervals” [Featonby Col. 16 Lines 48-51]. “These fingerprints or profiles may be included in the resource-utilization data 138 and be mapped to VM instance types 130 and/or workload categories for the workload 136” [Featonby Col. 16 Lines -55-58].
based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; “In some examples, the optimization service 106 may further monitor the workload 136 for the life of the workload 136, and provide additional recommendation data 132 upon detecting events that result in a different VM instance type 130 being more optimized to support the workload than the current VM instance type 130 to which the VM instance 114 corresponds. For instance, the user 105 may provide an indication to the optimization service 106 that the workload 136 has undergone a configuration change (e.g., update, software change, traffic change, etc.) that will likely result in a change in the resource-utilization characteristics of the workload 136” [Featonby Col. 14-15 Lines 59-67, 1-3].
storing the holistic profile of each CEDPM in a vendor environment database, “Additionally, the service provider network 102 may include a data store 208 which may comprise one, or multiple, repositories or other storage locations for persistently storing and managing collections of data such as databases, simple files, binary, and/or any other data. The data store 208 may include one or more storage locations that may be managed by one or more database management systems” [Featonby Col. 16 Lines 16-23, Fig. 2 Examiner notes the location of Workload Categories 220 within Data Store 208 in figure 2].
making a first determination that the resource utilization value of the first resource does not exceed a predetermined maximum resource utilization level; “At 1806, the service provider network 102 may determine that the actual utilization rate is different than a desired utilization rate specified for the user account” [Featonby Col. 38 Lines 10-12]. “To determine performance 1722, the optimization service 106 may compare throughput of data, such as overall utilization 1720) for the respective compute type (e.g., CPU, memory, disk, GPU, network throughput, etc.), versus the baseline and/or across the different chipset models. In this way, the optimization service 106 may determine how performant one chipset model is compared to another chipset model. Although illustrated as being CPU usage, the performance 1722 may be determined for one or more of the dimensions of compute (e.g., CPU, memory, disk, GPU, and network throughput) for the different chipset models and/or device IDs 1714” [Featonby Col. 36-37 Lines 64-67, 1-9]. “The optimization service may implement the techniques described herein at various stages in a life cycle of a workload to help optimize the performance of the workload, and reduce underutilization of computing resources” [Featonby Col. 5 Lines 16-20].
generating, based on the second determination, a recommendation to modify the resource utilization value of the first resource, wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization “The workload categories may further be associated with the VM instance types that are optimized for the resource-utilization characteristics of the represented workloads. In this way, when resource-utilization characteristics are obtained from a description provided by a new user, or through actual utilization data throughout the life of a workload, the resource-utilization characteristics may be mapped to the "closest" resource-utilization model of a predefined workload category, and the associated VM instance types for that workload category may be provided as recommendations to optimize performance of the workload” [Featonby Col. 7 Lines 12-23]. “In this way, performance metrics may be assigned to the underlying computing device on which a VM instance is provisioned to help determine an optimized VM instance type based on the computing device that is to be utilized. In an example where a workload is migrated from a less compute-performant device onto a more compute-performant device, the optimization service may select a new VM instance type based in part on a ratio of the performance between the two devices. In this way, the optimization service may select a new VM instance type that may not need be allocated as much compute power of the more compute-performance computing device” [Featonby Col. 7 Lines 49-60].
and sending the recommendation “FIG. 11 illustrates a flow diagram of an example method for determining that a new VM instance type is more optimized to support a workload than a current VM instance type, recommending the new VM instance type to a user account associated with the workload, and migrating the workload to the new VM instance type” [Featonby Col. 2 Lines 44-49].
and at least a portion of the holistic profile of the first CEDPM to a user of the first CEDPM, “The GUI 502 may allow the user 102 to select one of the VM instance types 130 to launch their workload 130 in an automated Fashion. As shown, the GUI 502 may present instance type 504, suitability 506, and explanations 508 for the recommended VM instance types 130. In the illustrated example, a first VM instance 510 may be storage optimized, have a suitability 506 of 4.5 out of 5 stars, and have an explanation 508 indicating that the VM instance type 510 delivers additional storage with sufficient compute for the workload 130” [Featonby Col. 24 Lines 37-43].
wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, “The GUI 300 may be presented on a user device 108, and accessible via a user account 242 and a console 122. In some examples, the GUI 300 may be part of the web-console wizard 212 that assists the user 105 in selecting an optimized or appropriate VM instance type 130 for a new workload 136” [Featonby Col. 19 Lines 62-67]. “The GUI 502 may comprise options through which a user 105 can select or choose a VM instance type 130. The GUI 502 may list different VM instances types 130 that have been determined by the optimization service 106 as being optimized for the workload 130 associated with the user account 242 through which the console 122 is accessed” [Featonby Col. 24 Lines 28-33]. “As a specific example, a user account 242 may be hosting their workload 1706 on a first VM instance type 130(1) that is supported by a first chipset model 1714. However, that 30 workload 1706 may be consuming too much CPU (and/or other computing resource) compared to a utilization goal or preference. The optimization service 106 may determine that a second VM instance type 130(2) that is supported by a second chipset model 1714 is more appropriate to host the workload 136 to achieve the utilization goal because, even if the second chipset model and second VM instance type 130(2) have less CPUs 1718 and/or vCPUs 1716, the ratio of performance metrics 1724 between the first and second chipset models 1714 may indicate that the second chipset model 1714 will still achieve lower utilization, and the same or higher throughput, to support the workload 1706. The optimization service 106 may select VM instance types 130 based at least in part on the performance metrics 1724 for the underlying computing resources of the supporting devices” [Featonby Col. 37 Lines 28-45].
initiating a recommendation monitoring service to track whether the recommendation is implemented by the user, and implementing, by the user, the recommendation, “Using this recommendation data 132, the user 105 can make a more informed decision as to what VM instance type 130 to utilize to support their workload 130, check a box next to the VM instance type 130 they desire, and further provide input into a select instance type control 516. Upon selecting the instance type, selection data 518 may be sent from the user device 108, over the network(s) 118, to the service provider network 102 (recommendation monitoring service) to indicate that the user 105 is requesting to have their workload 130 launched or supported by the first VM instance type 510” [Featonby Col. 24 Lines 53-62]. “The GUI 502 may allow the user 102 to select one of the VM instance types 130 to launch their workload 130 in an automated fashion” [Featonby Col. 24 Lines 34-36].
While Longo teaches a method of metadata monitoring where resource utilization data is computed and averaged then included in a set of resource parameters used in making optimal decisions for protection domain management. This can be combined with the teachings of Featonby to generate a profile with the resource utilization data and to provide the optimal decisions as suggestions to users. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo to incorporate the teachings of Featonby and include wherein the resource utilization value is obtained by employing a first set of machine learning (ML) models; identifying, based on the CM and for the first CEDPM and the second CEDPM, a plurality of modifications that have been performed on each identified resource over the DPOT; generating a holistic profile for each CEDPM based on at least the first resource and the second resource, the resource utilization value for each identified resource, the plurality of modifications, and the average resource utilization value for each identified resource; storing the holistic profile of each CEDPM in a vendor environment database, making a first determination that the resource utilization value of the first resource does not exceed a predetermined maximum resource utilization level; generating, based on the second determination, a recommendation to modify the resource utilization value of the first resource, wherein the recommendation is generated using the data protection landscape to proactively manage resource utilization and sending the recommendation and at least a portion of the holistic profile of the first CEDPM to a user of the first CEDPM, wherein the sending comprises: displaying, via a graphical user interface (GUI), a utilization performance of the first CEDPM by the user over the DPOT with respect to other users, initiating a recommendation monitoring service to track whether the recommendation is implemented by the user, and implementing, by the user, the recommendation. Doing so would allow for decisions made for optimization to be provided as recommendations to users before they are implemented. “In this way, the optimization service may provide recommendations to users that help improve performance of their workloads, and that also increase the aggregate utilization of computing resources of the service provider network” [Featonby Col. 5 Lines 27-31].
Longo in view of Featonby fails to teach making, based on the first determination, a second determination that the resource utilization value of the first resource is below the first average resource utilization value; generating, based on the second determination, a recommendation to modify the resource utilization value of the first resource.
However, Wu teaches:
making, based on the first determination, a second determination that the resource utilization value of the first resource is below the first average resource utilization value; “After determining the average resource utilization rate, find out the outgoing nodes whose resource utilization rate is greater than the average resource utilization rate, and the inbound nodes whose resource utilization rate is less than the average resource utilization rate” [Wu ¶ 43].
generating, based on the second determination, a recommendation to modify the resource utilization value of the first resource; “The service directory on each zero node is moved to each incoming node sequentially, so that the resource utilization of the incoming node and each zero node can reach the average resource utilization level” [Wu ¶ 43].
Wu is considered to be analogous to the claimed invention because it is in the same field of server selection for load balancing. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby to incorporate the teachings of Wu and include making, based on the first determination, a second determination that the resource utilization value of the first resource is below the first average resource utilization value; generating, based on the second determination, a recommendation to modify the resource utilization value of the first resource. Doing so would allow for further optimized usage of server resources. “The data processing method of the B2B cloud distribution platform system provided by the invention is based on the problems that the existing cloud distribution platform system has large data processing capacity and high requirements for server resources, and proposes a method based on load balancing, which can make the best use of server resources and improve the data processing efficiency of the platform system” [Wu ¶ 30].
Longo in view of Featonby in view of Wu fails to teach wherein the holistic profile for each CEDPM is generated by employing a second set of ML models.
However, Shetty teaches wherein the holistic profile for each CEDPM is generated by employing a second set of ML models; “In another example, if a new device is obtained by an organization, the hardware specs of the device can be inputted into the first ML model to generate a device profile. The device profile can be inputted into the third ML model to identify users or user groups to whom the new device is recommended” [Shetty ¶ 101].
Shetty is considered to be analogous to the claimed invention because it is in the same field of allocation of resources. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Wu to incorporate the teachings of Shetty and include wherein the holistic profile for each CEDPM is generated by employing a second set of ML models. Doing so would allow for the consideration of further CEDPM characteristics within the evaluations. “A second ML model can be trained to learn how a device is used by a user or users with similar roles. The second ML model can be trained using data related to the average lifespan of a device before replacement, software capabilities (maximum OS upgrade, support for certain apps, and so on), and general use cases of a device (e.g., the types of user to whom a particular device is assigned)” [Shetty ¶ 101].
Longo in view of Featonby in view of Wu in view of Shetty fails to explicitly teach wherein upon implementation, making the first CEDPM more user-friendly by reducing a number of steps required for subsequent operations.
However, Adam teaches wherein upon implementation, making the first CEDPM more user-friendly by reducing a number of steps required for subsequent operations. “And in some cases the system may automatically do certain actions that may no longer need explicit user interaction or automate them into a single step, especially when the prediction system for a particular user achieves a high level of reliability” [Adam ¶ 46]. “In step 806, user experience system 104 automates the next action. In automating the system is not presenting the action on the adapted user interface. Instead, it is performing the action for the user and then moving to the next predicted action and adapted user interface. Because of the decision in step 804, these actions are automated without risk for the software to perform an action not wanted by the user” [Adam ¶ 108].
Adam is considered to be analogous to the claimed invention because it is in the same field of interaction techniques based on graphical user interfaces. Longo in view of Featonby in view of Wu in view of Shetty presents a system wherein recommendations pertaining to CEPDMs are implemented by users. The teachings of Adam can be combined with this system to reduce a number of steps required for subsequent operations after the recommendation of Longo in view of Featonby in view of Wu in view of Shetty is implemented. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Wu in view of Shetty to incorporate the teachings of Adam and include wherein upon implementation, making the first CEDPM more user-friendly by reducing a number of steps required for subsequent operations. Doing so would allow for further ease of user for the user. “A less experienced user may not care to know the advanced features of a software application and may be using the application a few times to complete a straight forward task. Thus, the it is sometimes valuable to provide a simpler adapted user interface in some scenarios” [Adam ¶ 83].
With regard to claim 10, Longo in view of Featonby in view of Wu in view of Shetty in view of Adam teaches the method of claim 8, as referenced above. Longo further teaches:
wherein the first resource is a virtual machine, “As an example, a computer may be one or more server computers, cloud-based computers, cloud-based cluster of computers, virtual machine instances or virtual machine computing elements such as virtual processors, storage and memory, data centers, storage devices, desktop computers, laptop computers, mobile devices, or any other special-purpose computing devices” [Longo ¶ 22]. “Entities in system 300 may be virtualized entities. For example, multiple virtual block servers 312 may be included on a machine. Entities may also be included in a cluster, where computing resources of the cluster are virtualized such that the computing resources appear as a single entity” [Longo ¶ 55, Fig. 3C Examiner notes the volumes 330a-n in figure 3C].
wherein the virtual machine provides at least one computer-implemented service to the user. “Each slice service 220 may include one or more volumes (e.g., volumes 22la-x, volumes 22lc-y, and volumes 22le-z). Client systems (not shown) associated with an enterprise may store data to one or more volumes, retrieve data from one or more volumes, and/or modify data stored on one or more volumes” [Longo ¶ 38].
With regard to claim 12, Longo in view of Featonby in view of Wu in view of Shetty in view of Adam teaches the method of claim 8, as referenced above. Longo further teaches wherein the product configuration information specifies at least one selected from a group consisting of a type of each of a plurality of assets, a number of each type of the plurality of assets, a size of each of the plurality of assets, a type of an operating system, and a number of each type of a plurality of data protection policies. “In some examples a cloud-based QoS system may collect, from a plurality of the volumes 330a, 330b, 330n, on a per-volume basis, one or more real-time performance metrics for one or more compute processes executing on the one or more computer systems” [Longo ¶ 69]. “At block 415, at least one of a volume size, a volume activity level, or a number of client sessions operating on a volume is determined” [Longo ¶ 74].
With regard to claim 13, Longo in view of Featonby in view of Wu in view of Shetty in view of Adam teaches the method of claim 8, as referenced above. Longo further teaches wherein the recommendation specifies increasing a number of virtual machines protected by the first CEDPM … wherein the first CEDPM is provided to the user by a vendor. “The performance metrics may be used to perform load balancing operations by shifting volume compute responsibilities (i.e., read/write, hashing, compression) between storage services and/or protection domains” [Longo ¶ 16]. “For example, based on a number of factors, such as system metrics, client metrics, background process metrics, and client quality of service parameters, a compute load allocation may be adjusted, e.g., responsibility for a compute process on a first storage server node may be transferred to a second storage server node” [Longo ¶ 70]. “The data center 130 may represent an enterprise data center (e.g., an on-premises customer data center) that is build, owned, and operated by a company or the data center 130 may be managed by a third party (or a managed service provider) on behalf of the company, which may lease the equipment and infrastructure” [Longo ¶ 31].
Longo fails to teach increasing a number of virtual machines protected by the first CEDPM to utilize a full potential of the first CEDPM without exceeding a predetermined maximum resource utilization level.
However, Featonby teaches increasing a number of virtual machines protected by the first CEDPM to utilize a full potential of the first CEDPM without exceeding a predetermined maximum resource utilization level, “In some examples, computationally compatible workloads 136 and/or VM instance types 130 may include combinations of workloads 136 and/or VM instance types 130 that, in combination, (i) achieve maximum utilization of the underlying computing device 112, (ii) achieve a desired or goal utilization of the underlying computing device 112, and/or (iii) achieve a desired oversubscription of the underlying computing device 112 to ensure efficient utilization of the underlying resources of the computing device 112” [Featonby Col. 40 Lines 6-15]. “In this way, when a VM instance 114(1) is to be placed on a computing device 112, the optimization component 126 may identify a computing device 112 that already has a computationally complimentary VM instance 114(2) located thereon and place the VM instance 114(1) on that computing 55 device 112 to maximize the overall resource consumption” [Featonby Col. 39 Lines 51-56]. “For example, the auto-scaling component may provide a fast, efficient, and accurate way to match fleet capacity to usage. In some examples, the auto-scaling component may track the fleet's hosting metrics and determine when to add or remove instances 114 based on a set of guidelines, called policies. The auto-scaling component can adjust capacity in response to changes in demand to help ensure that the fleet of instances 114 has availability for bursts without maintaining an excessive amount of idle resources” [Featonby Col. 19 Lines 5-13].
With regard to claim 14, Longo in view of Featonby in view of Wu in view of Shetty in view of Adam teaches the method of claim 8, as referenced above. Longo further teaches:
wherein the recommendation specifies obtaining a third CEDPM “The performance metrics may be used to perform load balancing operations by shifting volume compute responsibilities (i.e., read/write, hashing, compression) between storage services and/or protection domains” [Longo ¶ 16]. “In some embodiments, the volumes may exist in multiple different protection domains (CEDPMs). For example, volume 330a may exist in a first protection domain, volume 330b may exist in a second protection domain, and volume 330n may exist in a third protection domain” [Longo ¶ 68].
in order to manage the resource utilization value of the first resource in the first CEDPM, wherein the first CEDPM is provided to the user by the vendor. “If, at operation 435, a protection domain does not have sufficient performance capacity to accept additional volumes, then control passes back to block 410 and the process 400 continues to collect metrics. By contrast, if at operation 435 a protection domain has sufficient performance capacity to accept additional volumes, then control passes to operation 440 and a load balancing algorithm is applied to rebalance compute load between available protection domains. In some examples an instruction may be generated that causes a processor to use the real-time performance metrics and the inertia parameter to determine whether to transfer responsibility for a compute process on a first storage server node to a second storage server node” [Longo ¶ 80].
Longo fails to teach wherein the recommendation specifies obtaining a third CEDPM from a vendor.
However, Featonby teaches wherein the recommendation specifies obtaining a third CEDPM from a vendor “In other examples, the service provider network may develop and offer new VM instance type(s) to increase the offerings of VM instance types for users. The optimization service may use various techniques, such as workload simulation, to determine that the new VM instance type is more optimized for the workload (or workload category to which the workload belongs) than the currently utilized VM instance type. For such reasons, and potentially other reasons, the optimization service may provide the user account with recommendations that the user migrate their workload from the current VM instance type to be hosted by a different VM instance type that is more optimized for the resource consumption/utilization of the workload” [Featonby Col. 6 Lines 20-33].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Longo (US 2022/0276907 A1) in view of Featonby (US 11,360,795 B2) in view of Wu (US 2024/0031429 A1) in view of Shetty (US 2023/0396487 A1) in view of Adam (US 2018/0364879 A1) in view of Shats (US 11,036,594 B1).
With regard to claim 9, Longo in view of Featonby in view of Wu in view of Shetty in view of Adam teaches the method of claim 8, as referenced above. Longo in view of Featonby in view of Wu in view of Shetty in view of Adam fails to teach wherein the first resource is a cloud disaster recovery policy, wherein the cloud disaster recovery policy is configured to recover a network-attached storage device that is utilized by the user.
However, Shats teaches wherein the first resource is a cloud disaster recovery policy, wherein the cloud disaster recovery policy is configured to recover a network-attached storage device that is utilized by the user. “Although it should be understood that in certain embodiments, a single protected site 150 may be in direct communication with an object store 100 of a data recovery system (e.g., via one or more IO filters), various embodiments may utilize one or more recovery groups and/or disaster recovery virtual appliances to facilitate efficient storage of data within the object storage 150” [Shats Col. 13 Lines 23-29, Fig. 5]. “Moreover, each virtual machine 151 has an IO filter 153 for intercepting data as it is written to local primary storage 152 during typical operation of the virtual machine 151 and for continuously replicating the data through a protection domain to the object store 100” [Shats Col. 13 Lines 35-40]. “In certain embodiments, the protected site 150 may utilize any datastore type compatible with the virtualization platform, including but not limited to Storage Area Network (SAN), Network Attached Storage (NAS) or server-integrated storage commonly referred to as hyper-converged infrastructure (HCI)” [Shats Col. 14 Lines 5-11].
The cloud disaster recovery methods of Shats which operate through protection domains can be combined with the protection domains of Longo. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Wu in view of Shetty in view of Adam to incorporate the teachings of Shats and include that the first resource is a cloud disaster recovery policy, wherein the cloud disaster recovery policy is configured to recover a network-attached storage device that is utilized by the user. Doing so would allow for a fast system recovery when encountering failures of different magnitudes. “Such embodiments provide a method for maintaining business continuity by resuming operations as quickly as possible (e.g., with minimal data interruption) upon detection of a failure of the protected site 150 (as reflected at Block 2002), even if resuming operations at the original protected site 150 is not possible for some period of time, such as during a fire, flood or other natural disaster or a severe hardware failure” [Shats Col. 9 Lines 11-18].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Longo (US 2022/0276907 A1) in view of Featonby (US 11,360,795 B2) in view of Wu (US 2024/0031429 A1) in view of Shetty (US 2023/0396487 A1) in view of Adam (US 2018/0364879 A1) in view of Venkatesh (US 2018/0157561 A1).
With regard to claim 11, Longo in view of Featonby in view of Wu in view of Shetty in view of Adam teaches the method of claim 8, as referenced above. Longo in view of Featonby in view of Wu in view of Shetty in view of Adam fails to teach wherein the first resource is a centralized protection policy, wherein the centralized protection policy is configured to protect a virtual machine utilized by the user.
However, Venkatesh teaches:
wherein the first resource is a centralized “In particular embodiments, failure of FSVMs 170 may be detected using the centralized coordination service” [Venkatesh ¶ 93].
protection policy, wherein the centralized protection policy is configured to protect a virtual machine utilized by the user. “Particular embodiments enable creation of protection domains comprising one or more consistency groups of VMs, volume groups, or other elements. Such protection domains may be scheduled for snapshots at regular intervals (e.g., every 12 hours) for disaster recovery or other purposes” [Venkatesh ¶ 61]. “A custom replication policy may be configured for the VFS 202, and the ability may be provided to map the VFS 202's configuration between sites to provide disaster recovery of virtual file-services across geographical locations. Particular embodiments may provide the ability to protect individual shares or share groups by protecting the volume group(s) used for file-services storage, e.g., by adding them to a protection domain” [Venkatesh ¶ 146].
Venkatesh is considered to be analogous to the claimed invention because it is in the same field of protected arrangements for virtualization. The centralized protection policy of Venkatesh to protect volumes within a protection domain can be combined with the protection domains of Longo. Therefore, it would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Longo in view of Featonby in view of Wu in view of Shetty in view of Adam to incorporate the teachings of Venkatesh and include that the first resource is a centralized protection policy, wherein the centralized protection policy is configured to protect a virtual machine utilized by the user. Doing so would allow for service recovery across geographical locations. “A custom replication policy may be configured for the VFS 202, and the ability may be provided to map the VFS 202's configuration between sites to provide disaster recovery of virtual file-services across geographical locations” [Venkatesh ¶ 146].
Response to Arguments
Applicant's arguments filed 10/23/2025 have been fully considered but they are not persuasive. Applicant argues in substance:
I. Independent claim 15 recites substantially similar limitations. Turning to the cited art,
neither Longo nor Featonby teach generating, using machine learning models, a holistic profile
and storing the holistic profile in a vendor environment database, wherein the vendor environment database maintains a data protection landscape, wherein the recommendation is generated by the data protection landscape which upon implementation by the user, making the data protection
module more user-friendly by reducing a number of steps required for subsequent operations, as
now recited in at least limitations (i)-(iv).
Accordingly, Longo, and Featonby, whether viewed separately or in combination, fail to
disclose or render obvious each and every limitation of the independent claims 1 and 15. Further,
because the Examiner has failed to establish a prima facie case of obviousness, the Applicant is
under no obligation to submit evidence of non-obviousness. See MPEP § 2142.
a) Examiner respectfully disagrees. Longo in view of Featonby teaches and storing the holistic profile in a vendor environment database, [Featonby Col. 16 Lines 16-23, Fig. 2] wherein the vendor environment database maintains a data protection landscape, [Longo ¶ 68] wherein the recommendation [Featonby Col. 7 Lines 12-23] is generated by the data protection landscape [Longo ¶ 79]. The system of Longo has a data protection landscape comprising storage servers, modifications are made to user resource utilization in this system based on storage server allocation. When combined with Featonby holistic profiles are generated for the hardware elements which are stored within a vendor environment database.
Applicant’s further arguments with respect to claim(s) 1 and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
II. Turning to the cited art, neither Longo, Featonby, or Wu teach generating, using machine learning models, a holistic profile and storing the holistic profile in a vendor environment database, wherein the vendor environment database maintains a data protection landscape, wherein the recommendation is generated by the data protection landscape which upon implementation by the user, making the data protection module more user-friendly by reducing a number of steps required for subsequent operations, as now recited in at least limitations (i)-(iv).
Accordingly, Longo, Featonby, and Wu, whether viewed separately or in combination, fail to disclose or render obvious each and every limitation of the independent claim 8. Further, because the Examiner has failed to establish a prima facie case of obviousness, the Applicant is under no obligation to submit evidence of non-obviousness. See MPEP § 2142.
a) Examiner respectfully disagrees. Examiner respectfully disagrees. Longo in view of Featonby teaches and storing the holistic profile in a vendor environment database, [Featonby Col. 16 Lines 16-23, Fig. 2] wherein the vendor environment database maintains a data protection landscape, [Longo ¶ 68] wherein the recommendation [Featonby Col. 7 Lines 12-23] is generated by the data protection landscape [Longo ¶ 79]. The system of Longo has a data protection landscape comprising storage servers, modifications are made to user resource utilization in this system based on storage server allocation. When combined with Featonby holistic profiles are generated for the hardware elements which are stored within a vendor environment database.
Applicant’s further arguments with respect to claim(s) 8, 10, and 12-14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
III. In view of the above, the Examiner's contentions and the cited art do not support an obviousness rejection of amended independent claims 1, 8, and 15. Claims 2-7, 9-14, and 16-20 depend, either directly or indirectly, from claims 1, 8, and 15. Accordingly, the Examiner's contentions and the cited art also do not support an obviousness rejection of claims 2-7, 9-14, and 16-20, and withdrawal of the rejection is respectfully requested.
a) Applicant’s further arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Examiner respectfully requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
When responding to this Office Action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
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/A.F.R./Examiner, Art Unit 2197
/BRADLEY A TEETS/Supervisory Patent Examiner, Art Unit 2197