DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-3, 6-11, and 14-19 are pending in this application.
Information Disclosure Statement
The IDS filed on 12/23/2025 has been considered.
Response to Arguments
Applicant’s arguments regarding the rejections of claims 1-20 under 35 U.S.C. 112b have been fully considered and are persuasive. The rejections have been withdrawn. However, new 35 U.S.C. 112b rejections are applied to claims 1-3, 6-11, and 14-19 based on the amendments.
Applicant’s arguments regarding the rejections of claims 18-20 under 35 U.S.C. 101 as being directed to non-statutory subject matter have been fully considered and are persuasive. The rejections of 18-20 are withdrawn.
Applicant's arguments regarding the rejections of claims 1-20 under 35 U.S.C. 101 abstract idea have been fully considered but they either not persuasive or moot.
Regarding the 35 U.S.C. 101 abstract idea rejections, the applicant argues the following in the remarks:
For example, the limitations of "monitoring stored time series data during execution of each workload of the plurality of workloads, calculating a delta value based on changes in the stored time series data, and predicting time series data values for a future time window, wherein the future time window is automatically adjusted by a window self-adjust model," or "calculating a variance between the delta value and a previously calculated delta value, and based on the variance being above a threshold, automatically training the window self-adjust model using workload data associated with the previously calculated delta value" does not describe a process that can be performed by a human. For example adjustment of the future time window are not performed by a human, but are rather performed by the model architecture.
The specification recites an improvement.
Examiner has thoroughly considered Applicant’s arguments, but respectfully finds them unpersuasive for at least the following reasons:
As to point (a), the examiner respectfully disagrees. Monitoring storage time series data is an insignificant extra solution activity. Calculating a data value is a mental process since humans can make mental computations. Predicting time series data values for a future time window is a mental process since humans can observe data to determine a pattern and make predictions. Adjusting the future time window is a mental process since humans can just mentally think that for example the future time window should be changed from 1 day to 1 week. Calculating a variance is also a mental computation. Training as described in the specification also includes mental computations.
As to point (b), the examiner respectfully disagrees. The specification discloses an
improvement, but the claims do not recite all the steps necessary to realize the improvement (see
MPEP 2106.04(d)(1) if the specification sets forth an improvement in technology, the claim must
be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim
includes the components or steps of the invention that provide the improvement described in the
specification.).
Applicant's arguments regarding the 35 U.S.C. 103 rejections of claims 1-20 have been fully considered but they are unpersuasive or moot.
Regarding the 35 U.S.C. 103 rejections, the applicant argues the following in the remarks:
Zhu, Das, Suarez, and Tajima fail to disclose or suggest features such as "monitoring stored time series data during execution of each workload of the plurality of workloads, calculating a delta value based on changes in the stored time series data, and predicting time series data values for a future time window, wherein the future time window is automatically adjusted by a window self-adjust model," or "calculating a variance between the delta value and a previously calculated delta value, and based on the variance being above a threshold, automatically training the window self-adjust model using workload data associated with the previously calculated delta value.”
Examiner has thoroughly considered Applicant’s arguments, but respectfully finds them unpersuasive for at least the following reasons:
As to point (a), Applicant does not point to how the cited references fail to teach this limitations. As shown in the rejection below, these references do teach these limitations.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 6-11, and 14-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 1, 9, and 17 (line numbers refer to claim 1):
Lines 25-26 recite “training the window self-adjust model using workload data associated with the previously calculated delta value”, but in line 21 recites “calculating a delta value based on changes in the stored time series data”. Therefore, it is unclear what workload data is associated with a previously calculated delta value when a delta value is calculated using stored time series data.
Claims 2-3, 6-8, 10-11, 14-16, and 18-19 are dependent claims of claims 1, 9, and 17, and fail to resolve the deficiencies of claims 1, 9, and 17, so they are rejected for the same reasons.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 6-11, and 14-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
As per claim 1, in step 1 of the 101 analysis, the examiner has determined that the claim
is directed to a method. Therefore, the claim is directed to one of the four statutory categories of invention.
The limitation “of managing workload requests” is an intended use limitation and does not have weight.
In step 2A prong 1 of the 101 analysis, the examiner has determined that the claim recites
a judicial exception. Specifically, the limitations “classifying the user according to a user classification model, the user classification model configured to group the user with one or more other users based on a similarity between the traffic plan and another traffic plan of the one or more other users”, “classifying each workload of the plurality of workloads according to the workload model, the workload model configured to classify each workload of the plurality of workloads based on a plurality of parameters”, “assigning each workload of the plurality of workloads into one or more workload groups based on the classifying of each workload of the plurality of workloads”, “calculating a delta value based on changes in the stored time series data, and predicting time series data values for a future time window, wherein the future time window is adjusted by a window self-adjust model; and calculating a variance between the delta value and a previously calculated delta value, and based on the variance being above a threshold, training the window self-adjust model using workload data associated with the previously calculated delta value” are mental processes. Humans can mentally classify the users into groups based on certain criteria since humans can use their judgement to categorize users. Classifying each workload is a mental process because humans can mentally judge what type of workload each workload is based on parameters. Humans can mentally group workloads based on the classifications of workloads. Calculating the delta value and calculating the variance is a mental process since humans can mentally make mathematical computations. Adjusting the future time window is a mental process since humans can think of change in a time window. The training involves mathematical computations that can be performed mentally.
In step 2A prong 2 of the 101 analysis, the examiner has determined that the additional
elements, alone or in combination do not integrate the judicial exceptions into a practical
application for the following rationale:
The limitations "receiving a workload job request from a user in a multi-tenant network", “each workload of the plurality of workloads including time series data configured to be stored in a time series database (TSDB)”, “inputting workload information to a workload model”, “executing each workload of the plurality of workloads, wherein a workload is executed according to the workload type and the amount of storage associated with the workload”, “monitoring stored time series data during execution of each workload of the plurality of workloads” represent insignificant, extra-solution activities. The term "extra-solution activity" can be understood as "activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim" (MPEP 2106.05(g)). The examiner has determined that the limitations "receiving a workload job request from a user in a multi-tenant network", “each workload of the plurality of workloads including time series data configured to be stored in a time series database (TSDB)”, “inputting workload information to a workload model”, “executing each workload of the plurality of workloads, wherein a workload is executed according to the workload type and the amount of storage associated with the workload”, “monitoring stored time series data during execution of each workload of the plurality of workloads” are directed to mere data gathering activities which is a category of insignificant extra-solution activities (MPEP 2106.05(g)).
The limitations “the request specifying a plurality of workloads and a traffic plan specific to the user”, “a workload model that is specific to the user”, and “the plurality of parameters including at least a workload type and an amount of storage associated with each workload of the plurality of workloads” merely describe attributes of the technological environment in with the abstract idea is operating. The courts have identified that generally linking the use of a judicial exception into a technological environment does not integrate a judicial exception into a practical application (MPEP 2106.04(d)(I)).
The limitation “automatically” applies judicial exceptions on a generic computer "Alappat 's rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court's Bilski and Alice Corp. decisions" so therefore performing judicial exceptions automatically does not integrate the judicial exceptions into a practical application (MPEP 2106.05(b)).
In step 2B of the 101 analysis, the examiner has determined that the additional elements,
alone or in combination do not recite significantly more than the abstract ideas identified above
for the following rationale:
The limitations "receiving a workload job request from a user in a multi-tenant network", “each workload of the plurality of workloads including time series data configured to be stored in a time series database (TSDB)”, “inputting workload information to a workload model”, “executing each workload of the plurality of workloads, wherein a workload is executed according to the workload type and the amount of storage associated with the workload”, “monitoring stored time series data during execution of each workload of the plurality of workloads” represent insignificant, extra-solution activities. The limitations "receiving a workload job request from a user in a multi-tenant network", “each workload of the plurality of workloads including time series data configured to be stored in a time series database (TSDB)”, “inputting workload information to a workload model”, “executing each workload of the plurality of workloads, wherein a workload is executed according to the workload type and the amount of storage associated with the workload”, “monitoring stored time series data during execution of each workload of the plurality of workloads” are well-understood, routine, or conventional because they are directed to "receiving or transmitting data" or “storing and retrieving information in memory” (MPEP 2106.05(d)). These are additional elements that the courts have recognized as well understood, routine, or conventional (MPEP 2106.05(d)). The citation of court cases in the MPEP meets the Berkheimer evidentiary burden since citation of a court case in the MPEP is one of the 4 types of evidentiary support that can be used to prove that the additional elements are well-understood, routine, or conventional (see 125 USPQ2d 1649 Berkheimer v. HP, Inc.). Thus, the limitations do not amount to significantly more than the abstract idea.
The limitations “the request specifying a plurality of workloads and a traffic plan specific to the user”, “a workload model that is specific to the user”, and “the plurality of parameters including at least a workload type and an amount of storage associated with each workload of the plurality of workloads” merely describe attributes of the technological environment and therefore do not amount to significantly more than the exception itself (MPEP 2106.05(h)).
The limitation "automatically” applies judicial exceptions on a generic computer and therefore does not provide significantly more.
As per claim 9, it is an apparatus claim of claim 1, so it is rejected for similar reasons. Additionally, it recites “one or more computer processors that comprise: a processing unit including a processor” which are generic computing components that neither integrate the judicial exceptions into a practical application nor recite significantly more.
As per claim 17, it is a non-transitory computer program product claim of claim 1, so it is rejected for similar reasons. Additionally, it recites “a non-transitory computer program product comprising a storage medium readable by one or more processing circuits, the storage medium storing instructions executable by the one or more processing circuits to perform a method” which attempts to apply the judicial exceptions onto generic computing components. The generic computing components neither integrate the judicial exceptions into a practical application nor recite significantly more.
As per claim 2 (and similarly for claims 10 and 18), it recites a mental process. The limitation recited in claim 2 is a mental process for the same reasons as described above.
As per claim 3 (and similarly for claims 11 and 19), it recites mental processes. Classifying is a mental process as explained above. Defining a vector space, constructing a workload type vector, a storage size vector, and calculating a vector angle can be performed with just pen and paper.
As per claim 6 (and similarly for claim 14), it recites “the revision model configured to calculate a variance between one or more parameters of the stored time series data and one or more parameters of the predicted data values” which include mental processes since humans can mentally compute a variance. Additionally, it recites “inputting the predicted data values to a revision model” which is an insignificant extra solution activity that is well-understood, routine, or conventional because it is directed to "receiving or transmitting data" or (MPEP 2106.05(d)). Therefore, the additional elements neither integrate the judicial exceptions into a practical application nor recite significantly more.
As per claim 7 (and similarly for claim 15), it recites “adjusting the workload model based on the variance” which is a mental process because the workload model can include a mathematical formula or a collection of vectors and humans can mentally modify a mathematical formula and humans can use a pen and paper to modify vectors.
As per claim 8 (and similarly for claim 16), it recites “incorporating the workload groups into a federated model associated with a plurality of tenants in the multi-tenant network” which is a mental process. It is a mental process because a federated model could be a mathematical formula and humans can mentally create a mathematical formula.
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, 2, 6-10, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 20160232036 A1 hereinafter Zhu), in view of Das et al. (US 11941014 B1 hereinafter Das), in view of Suarez Fuentes et al. (US 20130083806 A1 hereinafter Suarez), and further in view of Tajima et al. (US 20180275642 A1 hereinafter Tajima).
Zhu, Das, Suarez, and Tajima were cited in a prior office action.
As per claim 1, Zhu teaches the invention substantially as claimed including a method of managing workload requests, the method comprising: receiving a workload job request from a user in a multi-tenant network, the request specifying a plurality of workloads and a traffic plan specific to the user, each workload of the plurality of workloads including time series data configured to be stored in a database ([0045] workloads submitted by the cloud consumers 136; [0051] The workload profile (166) may be represented as a time series resource profile vector (158) that provides resource utilization characterizations; [0083] adds the profile to a workload resource usage profile database; [0046] one or more Quality-of-Service (Qos) metrics 126 (e.g., response time) of the cloud consumer's workload (e.g., application); [0047] The cloud consumer's QoS requirements 126 may include deadlines to complete particular jobs or tasks, number of CPUs, amount of memory, actual resources used in a particular amount of time; The cloud is a multi-tenant network since consumers can be considered tenants. A traffic plan is taught since the instant specification recites in [0072] that “The traffic plan specifies, for example, storage size and locations, and timing of execution of workloads” and Zhu discloses QoS metrics of the cloud consumer’s workload which include a deadline for completion and an amount of memory.);
inputting workload information to a workload model that is specific to the user, and classifying each workload of the plurality of workloads according to the workload model, the workload model configured to classify each workload of the plurality of workloads based on a plurality of parameters, the plurality of parameters including at least a workload type and an amount of storage associated with each workload of the plurality of workloads ([0051] The WPPI system 102 uses one or more models to forecast changes in the nature and character of the cloud consumer's workloads, the users' demand and cloud resource availability; [0049] The WPPI system 102 provides the cloud consumer 136 a way to estimate (e.g., model) the workload resource usage profiles 166 for the workloads submitted; [0071] The WPPI system 102 (e.g., workload resource estimation profiler 114 model) generates a resource vector (158, 604) of the metrics measured by the workload profiler. For example, the workload profiler 114 measures the one or more metrics identified with a particular sensitivity, criticality, or influence, or a combination thereof for meeting particular QoS guarantees. Some workloads may exhibit a workload signature identified as CPU-intensive, network bandwidth-intensive, memory-intensive; [0051] The workload resource estimation profiler 114 model characterizes the workloads (118, 120) submitted by the cloud consumers 136 as the workloads utilize resources (e.g., resource utilization metrics 168) across time (e.g., utilization of CPU, memory, disk, and network); [0073] When the WPPI system's 102 workload profiler 114 model determines the resource demand estimation 166, the resources utilization to achieve the QoS guarantee 126 for the workload may be known (e.g., the requirements for CPU, network bandwidth, and memory));
assigning each workload of the plurality of workloads into one or more workload groups based on the classifying of each workload of the plurality of workloads ([0046] the WPPI system 102 may use the resource usage estimation profile 166 to determine whether to consolidate workloads (118, 120) because the workloads' respective peak resources utilization (126) peaks at different times; [0035] The workload resource utilization profile may be represented as a time series resource profile vector 158 so that for example two workloads (118, 120) identified as CPU intensive may be combined to use a CPU because the time series CPU utilization of the respective two workloads (118, 120) require the CPU at different times.);
executing each workload of the plurality of workloads, wherein a workload is executed according to the workload type and the amount of storage associated with the workload ([0033] the WPPI system 102 uses the performance interference model 130 to automatically determine the workload types (118, 120, 112, 154) that may be optimally executed using the same or different hardware infrastructure resources; [0047] The cloud provider 134 accepts cloud consumer submitted workloads (118, 120) for execution using in the resources (144, 146, 148, 150, 160) of the cloud provider 134. The cloud provider 134 may attempt to accommodate as many workloads as possible, while meeting the QoS guarantees 126 for each of the cloud consumers 136. The cloud consumer's QoS requirements 126 may include deadlines to complete particular jobs or tasks, number of CPUs, amount of memory, actual resources used in a particular amount of time.);
monitoring stored time series data during execution of each workload of the plurality of workloads, calculating a delta value based on changes in the stored time series data, and predicting time series data values for a future time window ([0042] monitor changes to the workload over time (e.g., a workload profile may change workload types over time); [0042] The resource estimation profiler 114 uses a time series approach to forecast the workload profiles; [0086] forecast changes (e.g., resource utilization requirements) over a time period; [0083] adds the profile to a workload resource usage profile database; [0051] The workload resource estimation profiler 114 model characterizes each workload in order to determine resource utilization requirements by monitoring the workload as the WPPI system 102 processor executes (e.g., tests and/or models) the workload. The workload resource estimation profiler 114 model calculates a resource profile vector 158 for each workload based on the resources consumed and how (e.g., the manner in which) the resources are consumed. The workload profile (166) may be represented as a time series resource profile vector (158) that provides resource utilization characterizations; [0044] The resource estimation profile 166 for the workloads and the resources (e.g., hardware infrastructure resources—a server fails over to another server) used by the workloads; claim 21 determining, by the processor, resource usage changes for the first hardware infrastructure resource when the first hardware infrastructure resource processes both the first workload and the second workload compared to when the first hardware infrastructure processes the first workload as measured by the first resource utilization metric);
calculating a variance between the delta value and a previously calculated delta value ([0076] Performance modeling includes performing resource usage to service execution time relationship; The WPPI system 102 uses statistics (e.g., average and variance) of the sampled data points from the resource usage profile; claim 21 determining, by the processor, resource usage changes for the first hardware infrastructure resource when the first hardware infrastructure resource processes both the first workload and the second workload compared to when the first hardware infrastructure processes the first workload as measured by the first resource utilization metric; [0064] The metric values are used to regress against the change of the same metric (e.g., CPU, memory, storage, or network bandwidth/throughput) before and after the consolidation of the workload with one or more other workloads).
Zhu fails to teach each workload of the plurality of workloads including time series data configured to be stored in a time series database (TSDB); classifying the user according to a user classification model, the user classification model configured to group the user with one or more other users based on a similarity between the traffic plan and another traffic plan of the one or more other users; wherein the future time window is automatically adjusted by a window self-adjust model; based on the variance being above a threshold, automatically training the window self-adjust model using workload data associated with the previously calculated delta value.
However, Das teaches each workload of the plurality of workloads including time series data configured to be stored in a time series database (TSDB) (Col. 34 lines 39-41 A metadata service of a distributed time-series database may authoritatively, consistently, and durably store the metadata of time-series data; Col. 7 lines 39-47 To maintain high availability and high throughput for queries of time-series data, the time-series database 100 may use the metadata service 120 to identify the locations to which queries for particular time and space ranges are routed. The metadata index 122 may be highly scalable and highly available. In some embodiments, the metadata index 122 may support fast lookups (e.g., millisecond lookups) for billions of two-dimensional tiles and trillions of ingested data points per day.).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined Zhu with the teachings of Das to promote efficiency (see Das Col. 37 lines 19-20 The metadata service 120 may index metadata efficiently to support low-latency queries).
Zhu and Das fail to teach classifying the user according to a user classification model, the user classification model configured to group the user with one or more other users based on a similarity between the traffic plan and another traffic plan of the one or more other users; wherein the future time window is automatically adjusted by a window self-adjust model; based on the variance being above a threshold, automatically training the window self-adjust model using workload data associated with the previously calculated delta value.
However, Suarez teaches classifying the user according to a user classification model, the user classification model configured to group the user with one or more other users based on a similarity between the traffic plan and another traffic plan of the one or more other users ([0022] for each user, generating a user pattern comprising the traffic class of each user traffic flow associated with said user; [0023] grouping the plurality of users into one or more user groups based on the user pattern associated with each user; [0077] divides the user patterns into a number of user groups based on the user pattern associated with each user, each user group comprising user's whose user patterns are similar).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined Zhu and Das with the teachings of Suarez to promote efficiency (see Suarez [0012] This optimised traffic classification procedure can reduce the processing required to classify each new user traffic flow, increasing the efficiency of the traffic classification processing without the need for significant additional hardware or processing resources.).
Zhu, Das, and Suarez fail to teach wherein the future time window is automatically adjusted by a window self-adjust model; based on the variance being above a threshold, automatically training the window self-adjust model using workload data associated with the previously calculated delta value.
However, Tajima teaches wherein the future time window is automatically adjusted by a window self-adjust model; based on the variance being above a threshold, automatically training the window self-adjust model using workload data associated with the previously calculated delta value (Fig. 12; [0038] When this anomaly score exceeds a predetermined threshold, the anomaly detection system determines that there is an anomaly or a sign of an anomaly in the monitored system; [0141] The anomaly detection system of the present embodiments may be such that in the adjustment processing, the arithmetic device changes a window size for predicting the future time-series data based on a prediction capability of the predictive model so as to adjust the anomaly score such that the anomaly score for the operational data under normal operation falls within the predetermined range; [0078] Next, using the pairs of the window size and the internal state calculated above, the learning unit 122 of the server 12 learns a window size estimation model; [0057] The window size estimation model parameters 1D303 indicate parameters of a window size estimation model that dynamically changes the window size for calculation of an anomaly score so that anomaly scores of normal operation data may stay approximately the same; [0009] For this reason, an anomaly level (an anomaly score), that is calculated from a deviation between a prediction result and an observation result, can increase even during normal operation. In many cases, a threshold for the anomaly score is set and used for determining an anomaly based on whether an anomaly score exceeds the threshold).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined Zhu, Das, and Suarez with the teachings of Tajima to promote efficiency (see Tajima [0140] The structured prediction enables future prediction of data not only at a single point but also at a plurality of points representing a predetermined structure, allowing anomaly scores to be adjusted efficiently.).
As per claim 2, Zhu, Das, Suarez, and Tajima teach the method of claim 1. Zhu teaches wherein the workload model is further configured to classify each workload of the plurality of workloads based on a charge amount associated with each workload of the plurality of workloads ([0049] the WPPI system 102 uses models (114, 130, 116) and the affiliation rules 122 to optimize the number of QoS guarantees 126 of workloads that are satisfied or when a cost is associated with each workload then optimize the revenue).
As per claim 6, Zhu, Das, Suarez, and Tajima teach the method of claim 1. Zhu teaches to calculate a variance between one or more parameters of the stored time series data ([0076] The WPPI system 102 uses statistics (e.g., average and variance) of the sampled data points from the resource usage profile).
Additionally, Tajima teaches further comprising inputting the predicted data values to a revision model, the revision model configured to calculate a variance between one or more parameters of the stored time series data and one or more parameters of the predicted data values ([0071] Cumulative prediction error is an absolute value of the difference between a predicted value sequence and an observed value sequence at each time point; [0073] The predictive model 1N2 in FIG. 10 requires more calculations than the predictive model 1N1 in FIG. 9, but can output not only an expected value (i.e., a mean), but also a degree of dispersion (i.e., a variance), and can calculate not only an anomaly score based on cumulative prediction error; [0105] In the learning phase of these phases, the anomaly detection system learns a predictive model that predicts a time-series behavior of a monitored system based on operational data collected from each device and facility in the monitored system. The anomaly detection system also learns, using operational data and the predictive model, an error reconstruction model that reconstructs a prediction error sequence within a predetermined window size.).
As per claim 7, Zhu, Das, Suarez, and Tajima teach the method of claim 6. Zhu teaches further comprising adjusting the workload model based on the variance (claim 22 adjusting the workload profiler to forecast a degradation of the resource performance for the first hardware infrastructure resource and the second hardware infrastructure resource, respectively, wherein the degradation (as variance) represents performance interference resulting from using the first hardware infrastructure resource and the second hardware infrastructure resource together to process the first workload and the second workload.).
As per claim 8, Zhu, Das, Suarez, and Tajima teach the method of claim 1. Zhu teaches further comprising incorporating the workload groups into a federated model associated with a plurality of tenants in the multi-tenant network ([0051] The workload resource estimation profiler 114 model characterizes the workloads (118, 120) submitted by the cloud consumers 136 as the workloads utilize resources (e.g., resource utilization metrics 168) across time (e.g., utilization of CPU, memory, disk, and network); [0068] The WPPI system 102 may use a search algorithm (e.g., consolidation algorithm 416) to optimize revenue and reduce resource cost (418) (e.g., identifying cloud providers' resources to map the cloud consumers' submitted workloads). When the WPPI system 102 receives new applications (420) (e.g., workloads) submitted by consumers, the WPPI system 102 determines the optimal and sub-optimal mapping permutations for the workloads. The WPPI system 102 may interface to a resource management system (162) of the provider to deploy the consolidation strategy (e.g., mapping one or more workloads onto servers) based on the WPPI system 102 trained models).
As per claim 9, it is an apparatus claim of claim 1, so it is rejected for similar reasons. Additionally, Zhu teaches an apparatus for managing workload requests, comprising one or more computer processors that comprise: a processing unit including a processor ([0101] The computer system may include a processor, such as, a central processing unit (CPU); [0051] the WPPI system 102 processor executes (e.g., tests and/or models) the workload. The workload resource estimation profiler 114 model calculates a resource profile vector 158 for each workload based on the resources consumed and how (e.g., the manner in which) the resources are consumed. The workload profile (166) may be represented as a time series resource profile vector).
As per claims 10, 14, 15, and 16, they are apparatus claims of claims 2, 6, 7, and 8, respectively, so they are rejected for similar reasons.
As per claim 17, it is a computer program product claim of claim 1, so it is rejected for similar reasons. Additionally, Zhu teaches a non-transitory computer program product comprising a storage medium readable by one or more processing circuits, the storage medium storing instructions executable by the one or more processing circuits to perform a method ([0105] The disk drive unit may include a computer-readable medium in which one or more sets of instructions, e.g. software, can be embedded. Further, the instructions may perform one or more of the methods or logic as described herein. The instructions may reside completely, or at least partially, within the memory and/or within the processor during execution by the computer system.).
As per claim 18, it is a non-transitory computer program product claim of claim 2, so it is rejected for similar reasons.
Claims 3, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu, Das, Suarez, and Tajima, as applied to claims 1, 9, and 17 above, in view of Borowsky et al. (US 6366931 B1 hereinafter Borowsky).
Borowsky was cited in a prior office action.
As per claim 3, Zhu, Das, Suarez, and Tajima teach the method of claim 1. Zhu teaches wherein the workload model is further configured to classify each workload of the plurality of workloads by defining a vector space, constructing a workload type vector ([0069] FIG. 5 shows a graphical representation 500 of the workload profiles (e.g., time series vectors characterizing the workloads; [0051] The workload profile (166) may be represented as a time series resource profile vector (158) that provides resource utilization characterizations).
Zhu, Das, Suarez, and Tajima fails to teach a storage size vector, and calculating a vector angle.
However, Borowsky teaches a storage size vector, and calculating a vector angle (Col. 7 lines 24-30 With respect to a particular storage device and the corresponding set of workload units assigned to it, a new workload unit can also be represented as a point in this vector space by plotting the amount of each consumable constraint that the workload unit would consume if it were to be assigned to the storage device; Col. 7 lines 37-40 the angle of incidence between the storage device vector and the sum of the workload unit vectors for workload units on that storage device.).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined Zhu, Das, Suarez, and Tajima with the teachings of Borowsky to optimize resource assignment (see Borowsky Abstract optimization to generate an assignment plan for the workload units to the storage devices.).
As per claims 11 and 19, they are apparatus and non-transitory computer program product claims of claim 3, so they are rejected for similar reasons.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HSING CHUN LIN whose telephone number is (571)272-8522. The examiner can normally be reached Mon - Fri 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aimee Li can be reached at (571) 272-4169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/H.L./Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195