DETAILED ACTION
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 .
This action is responsive to communication filed on .
Claims 1-21 are subject to examination.
An IDS filed on 7/1/2022 has been fully considered and entered by the Examiner.
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.
Claim(s) 1-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mital et al. U.S. Patent Publication # 2019/0179725 (hereinafter Mital) in view of Gokan Khan et al. U.S. Patent Publication # 2022/0385542 (hereinafter Khan) further in view of Subramanian et al. U.S. Patent Publication # 2020/0104184 (hereinafter Subramanian)
With respect to claim 1, Mital teaches a data center comprising:
-a compute node (Fig. 1 element 106) to collect host telemetry data (i.e. private cloud may execute an application which generates metrics used to generate simulated workload or model of the application) (Paragraph 12, 13); and
-a network device (Fig. 1 element 102), the network device comprising:
-a network interface controller to collect network telemetry data (i.e. cloud application manager collects performance metrics such as number of transaction per minute, bandwidth performance metrics including data to determine when and whether and when the network is bottleneck) (Paragraph 16, 15, 20-21);
-a host interface to receive the host telemetry data from the compute node (i.e. performance metrics number of busy and idle threads, throughput performance metrics and performance metrics related to host health and application metrics)(Paragraph 16, 18-19); and
-circuitry to correlate the host telemetry data received from the compute node with the network telemetry data to provide processed telemetry data (Paragraph 16, 18-19), the processed telemetry data to identify one or more performance bottlenecks for a microservices based application (i.e. whether and when the network is bottleneck, whether the average page delivered requires significant bandwidth and whether content could be offloaded to a CDN) (Paragraph 16). Examiner would like to point out that, in the specification of the current application, it defines processed telemetry data as hints/outcome.
Although Mital implicitly shows the processed telemetry data to identify one or more bottlenecks for a microservices based application (as stated above), Khan explicitly teaches processed telemetry data to identify one or more bottlenecks (i.e. one bottleneck for which resource allocation configuration of microservices would be the only bottleneck for getting higher QoS) for a microservices based application (Paragraph 58, 61, 63). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Khan’s teaching in Mital’s teaching to come up with having telemetry data to identify one or more bottlenecks for microservices based application. The motivation for doing so would be appropriate remedial action can be taken to disperse the bottleneck by offloading the content to CDN or providing more bandwidth allocation.
Mital and Khan does not explicitly show upon container workload mapping data and/or container workload metadata associated with compute node-executed workloads; and processed telemetry data is to be accessed at least in part via at least one out-of-band data stream.
Subramanian teaches the circuitry to be configured based at least in part upon container workload mapping data and/or container workload metadata associated with compute node-executed workloads (i.e. POD manager allocate computer resources in edge server and/or can allocate resources on data center to perform one or more workloads serially or in parallel)(Paragraph 17-18, 23, 47-48); and processed telemetry data is to be accessed at least in part via at least one out-of-band data stream (i.e. telemetry collection and processing can occur out of band such that application performance counters and measurement and telemetry data are routed directly to accelerator) (Paragraph 50, 59). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Subramanian’s teaching to come up Mital and Khan’s teaching to come up with having container workload mapping data associated with compute node executed workloads and processed telemetry data is to be accessed at least in part via at least one out-of-band data stream. The motivation for doing so would to provide resource allocations for multitudes of workloads that run in parallel and using received telemetry information, the accelerator can be used to accelerate suggestions for resource allocations based on the telemetry data.
With respect to claim 2, Mital, Khan and Subramanian teaches the data center of claim 1, but Mital further teaches wherein the circuitry to store the host telemetry data received from the compute node in a remote storage node (i.e. transmitting performance metrics to the cloud application manager which is a remote storage node) (Paragraph 23, 26)
With respect to claim 3, Mital, Khan and Subramanian teaches the data center of claim 1, but Mital further teaches wherein the host telemetry data includes resource utilization (i.e. bandwidth performance metrics) and application performance metrics (i.e. transmit performance metrics regarding the performance of the application) (Paragraph 16)
With respect to claim 4, Mital, Khan and Subramanian teaches the data center of claim 1, but Mital further teaches wherein the network telemetry data includes per- workload network statistics (i.e. workload statistics such as 58,962 transactions per minute) including a number of dropped bytes and a latency (i.e. latency) between packets (Paragraph 25, 27)
With respect to claim 5, Mital, Khan and Subramanian teaches the data center of claim 1, but Mital further teaches wherein the circuitry to provide the processed telemetry data to a cloud service operator (i.e. transmitting performance metrics to the cloud application manager and providing the recommendation the host of the application which is inside the cloud application manager which functionally equivalent to cloud service operator)(Paragraph 23, 25)
With respect to claim 6, Mital, Khan and Subramanian teaches the data center of claim 1, but Khan further teaches wherein the circuitry to identify the performance bottlenecks by analyzing latency in network communication between microservices (Paragraph 52, 63, 134, 136)
With respect to claim 7, Mital, Khan and Subramanian teaches the data center of claim 1, but Mital further teaches wherein the network device is an infrastructure processing unit (Fig. 1 element 102) (Paragraph 13, 15-16)
With respect to claim 8, Mital teaches network device, the network device comprising:
-a network interface controller to collect network telemetry data (i.e. cloud application manager collects performance metrics such as number of transaction per minute, bandwidth performance metrics including data to determine when and whether and when the network is bottleneck) (Paragraph 16, 15, 20-21);
-a host interface to receive host telemetry data from a compute node (i.e. performance metrics number of busy and idle threads, throughput performance metrics and performance metrics related to host health and application metrics)(Paragraph 16, 18-19); and
-circuitry to correlate the host telemetry data received from the compute node with the network telemetry data to provide processed telemetry data (Paragraph 16, 18-19), the processed telemetry data to identify one or more performance bottlenecks for a microservices based application (i.e. whether and when the network is bottleneck, whether the average page delivered requires significant bandwidth and whether content could be offloaded to a CDN) (Paragraph 16). Examiner would like to point out that, in the specification of the current application, it defines processed telemetry data as hints/outcome.
Although Mital implicitly shows the processed telemetry data to identify one or more bottlenecks for a microservices based application (as stated above), Khan explicitly teaches processed telemetry data to identify one or more bottlenecks (i.e. one bottleneck for which resource allocation configuration of microservices would be the only bottleneck for getting higher QoS) for a microservices based application (Paragraph 58, 61, 63). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Khan’s teaching in Mital’s teaching to come up with having telemetry data to identify one or more bottlenecks for microservices based application. The motivation for doing so would be appropriate remedial action can be taken to disperse the bottleneck by offloading the content to CDN or providing more bandwidth allocation.
Mital and Khan does not explicitly show upon container workload mapping data and/or container workload metadata associated with compute node-executed workloads; and processed telemetry data is to be accessed at least in part via at least one out-of-band data stream.
Subramanian teaches the circuitry to be configured based at least in part upon container workload mapping data and/or container workload metadata associated with compute node-executed workloads (i.e. POD manager allocate computer resources in edge server and/or can allocate resources on data center to perform one or more workloads serially or in parallel)(Paragraph 17-18, 23, 47-48); and processed telemetry data is to be accessed at least in part via at least one out-of-band data stream (i.e. telemetry collection and processing can occur out of band such that application performance counters and measurement and telemetry data are routed directly to accelerator) (Paragraph 50, 59). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Subramanian’s teaching to come up Mital and Khan’s teaching to come up with having container workload mapping data associated with compute node executed workloads and processed telemetry data is to be accessed at least in part via at least one out-of-band data stream. The motivation for doing so would to provide resource allocations for multitudes of workloads that run in parallel and using received telemetry information, the accelerator can be used to accelerate suggestions for resource allocations based on the telemetry data.
With respect to claims 9-14 respectively, teaches same limitations as claims 2-7 respectively, therefore rejected under same basis.
With respect to claim 15, Mital teaches a method comprising:
-collecting, by a compute node, host telemetry data (i.e. performance metrics number of busy and idle threads, throughput performance metrics and performance metrics related to host health and application metrics)(Paragraph 16, 18-19)
-collecting, by a network interface controller in a network device, network telemetry data (i.e. cloud application manager collects performance metrics such as number of transaction per minute, bandwidth performance metrics including data to determine when and whether and when the network is bottleneck) (Paragraph 16, 15, 20-21);
- receiving, by circuitry in the network device, the host telemetry data from the compute node and the network telemetry data from the network interface controller (Paragraph 15-16, 18-19, 20-21); and
-correlating, by the circuitry in the network device, the host telemetry data received from the compute node with the network telemetry data to provide processed telemetry data (Paragraph 16, 18-19), the processed telemetry data to identify one or more performance bottlenecks for a microservices based application (i.e. whether and when the network is bottleneck, whether the average page delivered requires significant bandwidth and whether content could be offloaded to a CDN) (Paragraph 16). Examiner would like to point out that, in the specification of the current application, it defines processed telemetry data as hints/outcome.
Although Mital implicitly shows the processed telemetry data to identify one or more bottlenecks for a microservices based application (as stated above), Khan explicitly teaches processed telemetry data to identify one or more bottlenecks (i.e. one bottleneck for which resource allocation configuration of microservices would be the only bottleneck for getting higher QoS) for a microservices based application (Paragraph 58, 61, 63). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Khan’s teaching in Mital’s teaching to come up with having telemetry data to identify one or more bottlenecks for microservices based application. The motivation for doing so would be appropriate remedial action can be taken to disperse the bottleneck by offloading the content to CDN or providing more bandwidth allocation.
Mital and Khan does not explicitly show upon container workload mapping data and/or container workload metadata associated with compute node-executed workloads; and processed telemetry data is to be accessed at least in part via at least one out-of-band data stream.
Subramanian teaches the circuitry to be configured based at least in part upon container workload mapping data and/or container workload metadata associated with compute node-executed workloads (i.e. POD manager allocate computer resources in edge server and/or can allocate resources on data center to perform one or more workloads serially or in parallel)(Paragraph 17-18, 23, 47-48); and processed telemetry data is to be accessed at least in part via at least one out-of-band data stream (i.e. telemetry collection and processing can occur out of band such that application performance counters and measurement and telemetry data are routed directly to accelerator) (Paragraph 50, 59). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Subramanian’s teaching to come up Mital and Khan’s teaching to come up with having container workload mapping data associated with compute node executed workloads and processed telemetry data is to be accessed at least in part via at least one out-of-band data stream. The motivation for doing so would to provide resource allocations for multitudes of workloads that run in parallel and using received telemetry information, the accelerator can be used to accelerate suggestions for resource allocations based on the telemetry data.
With respect to claim 16, Mital, Khan and Subramanian teaches the method of claim 15, but Mital further teaches wherein the circuitry in the network device storing host telemetry data received from the compute node in a remote storage node (i.e. transmitting performance metrics to the cloud application manager which is a remote storage node) (Paragraph 23, 26)
With respect to claim 17, Mital, Khan and Subramanian teaches the method of claim 15, but Mital further teaches wherein the host telemetry data includes resource utilization (i.e. bandwidth performance metrics), application performance metrics (i.e. transmit performance metrics regarding the performance of the application) (Paragraph 16) and health indicators (i.e. performance metrics related to host health)(Paragraph 18)
With respect to claim 18, Mital, Khan and Subramanian teaches the method of claim 15, but Mital further teaches wherein the network telemetry data includes per- workload network statistics (i.e. workload statistics such as 58,962 transactions per minute)(Paragraph 25, 27)
With respect to claim 19, Mital, Khan and Subramanian teaches the method of claim 15, but Mital further teaches wherein the circuitry in the network device providing the processed telemetry data to a cloud service operator (i.e. transmitting performance metrics to the cloud application manager and providing the recommendation the host of the application which is inside the cloud application manager which functionally equivalent to cloud service operator)(Paragraph 23, 25)
With respect to claim 20, Mital, Khan and Subramanian teaches the method of claim 15, but Khan further teaches wherein the circuitry in the network device identifying the performance bottlenecks by analyzing latency in network communication between microservices (Paragraph 52, 63, 134, 136)
With respect to claim 21, Mital, Khan and Subramanian teaches the method of claim 15, but Mital further teaches wherein the network device is an infrastructure processing unit (Fig. 1 element 102) (Paragraph 13, 15-16)
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-21 have been considered but are moot in view of new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A). Doshi et al. U.S. Patent Publication # 2019/0007284 which teaches about obtaining telemetry data from the data storage device in response to receiving a data access request and store telemetry.
B). Field et al. U.S. Patent Publication # 2013/0018632
C). Singh et al. U.S. Patent Publication # 2020/0310658 which teaches about clustered computing system monitoring, fault prediction and remediation.
D). Thiel et al. U.S. Patent Publication # 2021/0126847.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DHAIRYA A PATEL whose telephone number is (571)272-5809. The examiner can normally be reached M-F 7:30am-4:00pm.
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, Kamal B Divecha can be reached at 571-272-5863. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
DHAIRYA A. PATEL
Primary Examiner
Art Unit 2453
/DHAIRYA A PATEL/ Primary Examiner, Art Unit 2453