Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The instant application having Application No. 18/646,680 filed on 4/25/2024 is presented for examination.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
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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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vishwakarma (US 2021/0034425) in view of Nandagopal (US 2021/0026704).
As per claim 1, Vishwakarma discloses a method, comprising:
obtaining, by a computing system, requests to be processed, the requests being executed by one or more processing threads running in a background process for a cloud infrastructure service (Paragraph 33 “In one embodiment of the invention, the aggregation of these values for any given feature, spanning multiple sampling points, may be referred herein as statistical data. By way of examples, these statistical data may include, but are not limited to, measurements or metrics relevant to backup storage system resource (e.g., compute, storage, virtualization, network, etc.) availability and utilization, measurements or metrics relevant to the input, execution, and output of a given background service task (e.g. garbage collection, data migration, data replication, update download, etc.), information describing the general configuration of the backup storage system, information describing the configuration of a given background service task, etc. Furthermore, the first feature set(s) may be derived from maintained histories (i.e., historical records) of statistical data.”);
receiving, by the computing system, historical information related to the background process for the cloud infrastructure service, the historical information comprising a performance distribution in background process (Paragraph 35 “In Step 304, a second feature set is obtained. In one embodiment of the invention, whereas the first feature set(s) (obtained in Step 300) may be derived from historical (or past) values representative of a collection of features, the second feature set may be derived from more recent or current, observed values of the collection of features. Accordingly, each first feature set may represent a separate training data set for optimizing the predictive model (generated in Step 302), while the second feature set may represent the testing data set through which a prediction may be produced.”);
evaluating, by the computing system, feasibility to meet an objective for completing the background process based at least in part on the obtained requests and the historical information related to the background process (Paragraph 15 “intelligently provisioning resources in storage systems, Specifically, one or more embodiments of the invention entails throttling the allocation of resources aiding in the performance of background service tasks on a backup storage system. That is, throughout a predicted span of a background service task, resources may be dynamically allocated towards the performance of the background service task at discrete time intervals within the predicted span, thereby improving overall system utilization.” See also, paragraph 31 “FIG. 3 shows a flowchart describing a method for predicting a background service task duration in accordance with one or more embodiments of the invention. The various steps outlined below may be performed by the task duration predictor residing on the backup storage system (see e.g., FIG. Further, while the various steps in the flowchart are presented and described sequentially, one of ordinary skill will appreciate that some or all steps may be executed in different orders, may be combined or omitted, and some or all steps may be executed in parallel.”);
performing, by the computing system, the action for the background process (Abstract “A method and system for intelligently provisioning resources in storage systems. Specifically, the method and system disclosed herein entail throttling the allocation of resources aiding in the performance of background service tasks on a backup storage system. That is, throughout a predicted span of a background service task, resources may be dynamically allocated towards the performance of the background service task at discrete time intervals within the predicted span, thereby improving overall system utilization.”).
Vishwakarma does not expressly disclose but Nandagopal discloses determining, by the computing system, an action to take for the background process based at least in part on the evaluation, the action being configured to effect gradual changes in the background process (Paragraph 46 “A detection interval is considered violated when an average throughput for workloads processed within the time period associated with the detection interval does not exceed the stated guaranteed minimum throughput of the SLO. Based in part on a number of detection intervals with a violation within a time period corresponding to a detection window, the throughput management module 220 may set an incremental or full throttle with respect to non-priority, interfering workloads.” See also, paragraph 55 “In step 308, the node computing device 106(1) determines whether to implement an incremental throttle. In this example, the node computing device 106(1) is configured to implement either a relatively small, incremental throttle, or a relatively large throttle, although the throttle rate can be dynamically-determined and different throttle rates can be used in other examples. In one example, the throttle limits the rate at which non-priority, interfering workloads are retrieved from queue(s) and processed, although other methods for limiting the rate at which non-priority workloads are handled can also be used in other examples.”).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Vishwakarma to include the teachings of Nandagopal because the incremental adjustments permit the combination to approach an optimal resource allocation gradually which increases efficiency.
As per claim 2, Vishwakarma further discloses wherein the background process is a first operation being performed in parallel to a second operation performed by the cloud infrastructure service (Paragraph 36 “Further, while the various steps in the flowchart are presented and described sequentially, one of ordinary skill will appreciate that some or all steps may be executed in different orders, may be combined or omitted, and some or all steps may be executed in parallel.”).
As per claim 3, Vishwakarma further discloses wherein the first operation performed in the background process is a garbage collection operation (Paragraph 20) and the second operation performed by the cloud infrastructure service is an object deletion operation (Paragraph 39 “In Step 402, a system log of the backup storage system is parsed. In one embodiment of the invention, the system log may represent a data object (e.g., a file, a table, a structure of arrays, a composite variable, etc.) that chronologically documents or records the utilization of backup storage system resources at prescribed sampling intervals. That is, the system log may maintain a point-in-time utilization history of one or more backup storage system resource types (e.g., compute resources, storage resources, virtualization resources, network resources, etc.). Further, the point-in-time utilization history of each backup storage system resource type may be broken down into various categories or features—e.g., a percentage of the resource type consumed for user-pertinent operations (e.g., data backup operations, data restore operations, data deduplication operations, etc.), a percentage of the resource type consumed for system-pertinent operations (e.g., background service tasks, etc.), a percentage of the resource type not utilized or idled, etc.”).
As per claim 4, Nandagopal further discloses wherein the performance distribution of the historical information comprises a moving average execution time of the requests by the one or more processing threads over a sliding window (Paragraph 46 “A detection interval is considered violated when an average throughput for workloads processed within the time period associated with the detection interval does not exceed the stated guaranteed minimum throughput of the SLO. Based in part on a number of detection intervals with a violation within a time period corresponding to a detection window, the throughput management module 220 may set an incremental or full throttle with respect to non-priority, interfering workloads.” See also, paragraph 55 “In step 308, the node computing device 106(1) determines whether to implement an incremental throttle. In this example, the node computing device 106(1) is configured to implement either a relatively small, incremental throttle, or a relatively large throttle, although the throttle rate can be dynamically-determined and different throttle rates can be used in other examples. In one example, the throttle limits the rate at which non-priority, interfering workloads are retrieved from queue(s) and processed, although other methods for limiting the rate at which non-priority workloads are handled can also be used in other examples.”).
As per claim 5, Nandagopal further discloses wherein the performance distribution of the historical information comprises a trend of changes in a moving average execution time of the requests by the one or more processing threads (Paragraph 46 “A detection interval is considered violated when an average throughput for workloads processed within the time period associated with the detection interval does not exceed the stated guaranteed minimum throughput of the SLO. Based in part on a number of detection intervals with a violation within a time period corresponding to a detection window, the throughput management module 220 may set an incremental or full throttle with respect to non-priority, interfering workloads.”).
As per claim 6, Vishwakarma further discloses wherein the objective is an amount of time allowed for the background process to complete the requests assigned to the background process (Paragraph 15 “In general, embodiments of the invention relate to a method and system for intelligently provisioning resources in storage systems, Specifically, one or more embodiments of the invention entails throttling the allocation of resources aiding in the performance of background service tasks on a backup storage system. That is, throughout a predicted span of a background service task, resources may be dynamically allocated towards the performance of the background service task at discrete time intervals within the predicted span, thereby improving overall system utilization.”).
As per claim 7, Vishwakarma further discloses wherein the gradual changes in the background process are changes in an expected execution time for processing the requests to meet the objective, wherein the expected execution time is shorter than and close to the objective while minimum resources are used for the background process (Paragraphs 27-30).
As per claim 8, Vishwakarma further discloses wherein the action is an increase, decrease or substantially the same in the expected execution time for processing the requests (Paragraph 30 “In Step 206, an allocation of backup storage system resources is regulated (or throttled) based at least on the forecast time-series (generated in Step 204). That is, in one embodiment of the invention, while the background service task (identified in Step 200) executes, backup storage system resources may be dynamically allocated, in accordance and proportional to the availability of backup storage system resources projected by the forecast time-series, towards supporting the execution of the background service task, Throttling of backup storage system resources allocation is described in further detail below with respect to FIG. 5.”).
As per claims 9-14, they are medium claims having similar limitations as cited in claims 1-8 and are rejected under the same rationale.
As per claims 15-20, they are system claims having similar limitations as cited in claims 1-8 and are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jung (US 2007/0136401) discloses a garbage collection apparatus and a method using the same are disclosed. The garbage collection method comprises: making a list of objects that must be deleted from a memory; calculating a predetermined residual time for responding to an external command; deleting the listed objects from the memory during the residual time; and storing a list of remaining objects that have not been deleted from the memory during the residual time. Accordingly, communication failure due to a response delay or timeout is prevented by distributed processing loads of garbage collection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY A MUDRICK whose telephone number is (571)270-3374. The examiner can normally be reached 9am-5pm Central Time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at (571)272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIMOTHY A MUDRICK/Primary Examiner, Art Unit 2198 6/30/2026
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