DETAILED ACTION
This office action is a response to an application filed 03/07/2025, wherein claims 1-20 are pending and ready for an examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/06/2025 was filed before the mailing date of the non-final action on 07/02/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-3, 5-9, 12-15, 17-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-4, 6, 8,12, 14 ad 21 of U.S. Patent No. 12271749 in view of Singh et al. (US 2020/0026560), hereinafter “Singh”.
Instant Application # 19073645
US patent # US12271749B2
1, 8 and 15 a system, a method comprising, and a non-transitory computer readable medium:
monitoring, by a processor, network traffic corresponding to a plurality of containerized workloads deployed in a virtual computing environment by determining a correspondence between nodes on which the plurality of containerized workloads are deployed;
determining, by the processor, one or more affinities between containerized workloads among the plurality of containerized workloads based at least in part on determined traffic characteristics between the plurality of containerized workloads;
collecting, by the processor, affinity information corresponding to the determined affinities over time based on multiple user actions;
generating, by the processor, an aggregate of the collected affinity information; and
scheduling, by the processor, execution of a containerized workload on a node in the virtual computing environment based at least in part on the aggregate of the collected affinity information.
1, 8, 14 and 21, a method for containerized workload scheduling, comprising:
monitoring network traffic between a first containerized workload deployed on a node in a virtual computing environment (VCE) and other containerized workloads in the VCE to determine affinities between the first containerized workload and the other containerized workloads in the VCE,
wherein the affinities are based on information derived from the monitoring of the network traffic between the first containerized workload and the other containerized workloads that change over time, such that the affinities characterize dynamic communication patterns of the network traffic between the first containerized workload and the other containerized workloads in the VCE to assess inter-containerized workload interactions in real time; and
scheduling, based, at least in part, on the determined affinities characterizing the dynamic communication patterns of the network traffic between the first containerized workload and the other containerized workloads, subsequent execution of a second containerized workload on the node on which the first containerized workload is deployed;
wherein the network traffic comprises flows characterized by flow sizes, the flows being classified flow types based on the flow size as mouse flows or elephant flows, the affinities being associated with the flow types.
2 and 9, The system of claim 1 and the method of claim 8, wherein the instructions further cause the system to:
assign weights to the determined affinities between the first containerized workload and the other containerized workloads based on the collected affinity information.
2. The method of claim 1, further comprising:
assigning respective scores to the affinities between the first containerized workload and the other containerized workloads; and
scheduling execution of the container to run the second containerized workload based, at least in part, on the assigned scores.
3 and 20, The system of claim 2 and the non-transitory of claim 20, wherein the instructions further cause the system to:
generate scores for nodes in the virtual computing environment based on the assigned weights; and
schedule execution of the second containerized workload on the node having the highest score.
2. The method of claim 1, further comprising:
assigning respective scores to the affinities between the first containerized workload and the other containerized workloads; and
scheduling execution of the container to run the second containerized workload based, at least in part, on the assigned scores.
5, 12 and 17, The system of claim 1, the method of claim 8 and the non-transitory of claim 15, wherein the node comprises a virtual computing instance or a hypervisor.
4. The method of claim 1, wherein the node comprises a virtual computing instance or a hypervisor.
6, 13 and 18, The system of claim 1, the method of claim 8 and the non-transitory of claim 15, wherein the instructions further cause the system to:
schedule execution of the second containerized workload on the node based additionally on an amount of computing resources available on the node.
6. The method of claim 1, further comprising scheduling, based, at least in part, on an amount of computing resources available to the node on which the first containerized workload is deployed, execution of the second containerized workload on the node on which the first containerized workload is deployed.
7, 14 and 19, The system of claim 1, the method of claim 8 and the non-transitory of claim 15, wherein the affinity information comprises data indicating frequencies of interactions between the first containerized workload and the other containerized workloads.
12. The method of claim 8, further comprising:
analyzing interactions between the plurality of containerized workload to determine correspondences between execution of the containerized workload and execution of a different containerized workload; and
scheduling execution of the different containerized workload on a node in the virtual computing system based, at least in part, on the analyzed interactions.
However, U.S. Patent No. 12271749 remain silent on collecting, by the processor, affinity information corresponding to the determined affinities over time based on multiple user actions; generating, by the processor, an aggregate of the collected affinity information.
Singh discloses collect affinity information corresponding to the determined affinities over time based on multiple user actions (¶0025, teaches a workload detection and classification model to dynamically classify workloads (i.e. affinities over time) to facilitate workload-based resource allocation in a distributed virtualization system, ¶0030, teaches workload affinity determination technique 1A00 can commence upon deploying instrumentation that serves to collect a set of observed I/O activity from virtualized entities, ¶0040, teaches the user or users interacting with the workload (e.g., user identifiers, etc.), ¶0064, teaches a set of observed I/O activity trace data associated with the earlier identified workloads (e.g., observed workloads 326) can be collected (operation 304). In the example shown, observed I/O activity trace data 128 (see FIG. 3A2) can be selected for observed workloads. The number of samples and/or period of time pertaining to the collected data can vary, ¶0067, teaches certain I/O activity in certain time periods might be more correlated to a respective workload than I/O activity in other time periods. Accordingly, only certain data from certain analysis periods are selected from trace data to be used in forming a workload detection model);
generate an aggregate of the collected affinity information (¶0046, teaches the generated workload detection and classification model 108 comprises relationships (e.g., correlations, weighted correlations, coefficients, dominance, etc.) between observed I/O activity (e.g., activity trace data) and workload type identifiers, ¶0064, teaches a set of observed I/O activity trace data associated with the earlier identified workloads (e.g., observed workloads 326) can be collected (i.e. aggregate) (operation 304).¶0083, teaches various workload-based resource allocation operations (e.g., workload migrations) can be generated (operation 180 3).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify US patent 12271749’s system with collecting, by the processor, affinity information corresponding to the determined affinities over time based on multiple user actions; generating, by the processor, an aggregate of the collected affinity information of Singh, in order to enable subsequent processing such as scheduling or resource allocation to be performed with greater accuracy, consistency and efficiency (Singh).
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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (US 2020/0026560), hereinafter “Singh” in view of Vyas et al. (US 2018/0203736), hereinafter “Vyas”.
With respect to claim 1, Singh discloses a system comprising:
a processor (¶0093); and
a memory storing instructions that, when executed by the processor (¶0093), cause the system to:
monitor network traffic between a first containerized workload deployed on a node in a virtual computing environment and other containerized workloads in the virtual computing environment to determine affinities between the first containerized workload and the other containerized workloads (¶0030, teaches the workload affinity determination technique 1A00 presents certain operations for implementing a computer-aided scheduling capability to allocate resources in a computing system based on observed resource usage. Specifically, workload affinity determination technique 1A00 can commence upon deploying instrumentation that serves to collect a set of observed I/O activity from virtualized entities, ¶0045, teaches workload detection agent 104 11 comprises an I/O trace monitor 106 to monitor (e.g., “trace”) a set of storage I/O activity 120 associated with various workloads (e.g., workload 174 1, . . . , workload 174 N) operating at certain virtualized entities (e.g., VE 172 111, . . . , VE 172 NMK, respectively) at distributed virtualization system 102);
collect affinity information corresponding to the determined affinities over time based on multiple user actions (¶0025, teaches a workload detection and classification model to dynamically classify workloads (i.e. affinities over time) to facilitate workload-based resource allocation in a distributed virtualization system, ¶0030, teaches workload affinity determination technique 1A00 can commence upon deploying instrumentation that serves to collect a set of observed I/O activity from virtualized entities, ¶0040, teaches the user or users interacting with the workload (e.g., user identifiers, etc.), ¶0064, teaches a set of observed I/O activity trace data associated with the earlier identified workloads (e.g., observed workloads 326) can be collected (operation 304). In the example shown, observed I/O activity trace data 128 (see FIG. 3A2) can be selected for observed workloads. The number of samples and/or period of time pertaining to the collected data can vary, ¶0067, teaches certain I/O activity in certain time periods might be more correlated to a respective workload than I/O activity in other time periods. Accordingly, only certain data from certain analysis periods are selected from trace data to be used in forming a workload detection model);
generate an aggregate of the collected affinity information (¶0046, teaches the generated workload detection and classification model 108 comprises relationships (e.g., correlations, weighted correlations, coefficients, dominance, etc.) between observed I/O activity (e.g., activity trace data) and workload type identifiers, ¶0064, teaches a set of observed I/O activity trace data associated with the earlier identified workloads (e.g., observed workloads 326) can be collected (i.e. aggregate) (operation 304).¶0083, teaches various workload-based resource allocation operations (e.g., workload migrations) can be generated (operation 180 3).
However, Singh remains silent on schedule, based at least in part on the aggregate of the collected affinity information, execution of a second containerized workload on the node on which the first containerized workload is deployed.
Vyas discloses schedule, based at least in part on the aggregate of the collected affinity information, execution of a second containerized workload on the node on which the first containerized workload is deployed (¶0012, i.e. Using a quantitative value to represent these hierarchical affinity relationships allows for the representation of a deployment scheme for a distributed service as an affinity distribution that is representative of the relationship between the various containers providing the distributed service, and if four containers deployed to a first node result in a certain level of performance, then four equivalent containers deployed to a second node with equivalent hardware specifications to the first node should yield a similar level of performance to the first four containers, ¶0021, i.e. a hierarchical map of a system 200 employing affinity based hierarchical container scheduling according to an example of the present disclosure. In an example, scheduler 140 may be a scheduler responsible for deploying containers (e.g., containers 152A-D, 160A-G, 260A-C, 262A-C) to nodes (e.g., nodes 112, 116, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, and 250) to provide a variety of distributed services”).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Singh’s workload affinity determination to collect a set of observed I/O activity from virtualized entities with schedule, based at least in part on the aggregate of the collected affinity information, execution of a second containerized workload on the node on which the first containerized workload is deployed of Vyas, in order to improve workload placement decisions, reduce inter-node communication overhead, decrease network latency and bandwidth consumption (Vyas).
With respect to claim 8, Singh discloses a method comprising:
monitoring, by a processor, network traffic corresponding to a plurality of containerized workloads deployed in a virtual computing environment by determining a correspondence between nodes on which the plurality of containerized workloads are deployed (¶0030, teaches the workload affinity determination technique 1A00 presents certain operations for implementing a computer-aided scheduling capability to allocate resources in a computing system based on observed resource usage. Specifically, workload affinity determination technique 1A00 can commence upon deploying instrumentation that serves to collect a set of observed I/O activity from virtualized entities, ¶0045, teaches workload detection agent 104 11 comprises an I/O trace monitor 106 to monitor (e.g., “trace”) a set of storage I/O activity 120 associated with various workloads (e.g., workload 174 1, . . . , workload 174 N) operating at certain virtualized entities (e.g., VE 172 111, . . . , VE 172 NMK, respectively) at distributed virtualization system 102);
determining, by the processor, one or more affinities between containerized workloads among the plurality of containerized workloads based at least in part on determined traffic characteristics between the plurality of containerized workloads (¶0030, teaches workload affinity determination technique 1A00 can commence upon deploying instrumentation that serves to collect a set of observed I/O activity from virtualized entities (i.e. plurality of containerized workloads), ¶0033, teaches determine the correlation metrics and/or affinities to a particular workload type, ¶0112, teaches packet characteristics include a version identifier, a packet or payload length, a traffic class, a flow label, etc.);
collecting, by the processor, affinity information corresponding to the determined affinities over time based on multiple user actions (¶0025, teaches a workload detection and classification model to dynamically classify workloads (i.e. affinities over time) to facilitate workload-based resource allocation in a distributed virtualization system, ¶0030, teaches workload affinity determination technique 1A00 can commence upon deploying instrumentation that serves to collect a set of observed I/O activity from virtualized entities, ¶0040, teaches the user or users interacting with the workload (e.g., user identifiers, etc.), ¶0064, teaches a set of observed I/O activity trace data associated with the earlier identified workloads (e.g., observed workloads 326) can be collected (operation 304). In the example shown, observed I/O activity trace data 128 (see FIG. 3A2) can be selected for observed workloads. The number of samples and/or period of time pertaining to the collected data can vary, ¶0067, teaches certain I/O activity in certain time periods might be more correlated to a respective workload than I/O activity in other time periods. Accordingly, only certain data from certain analysis periods are selected from trace data to be used in forming a workload detection model);
generating, by the processor, an aggregate of the collected affinity information (¶0046, teaches the generated workload detection and classification model 108 comprises relationships (e.g., correlations, weighted correlations, coefficients, dominance, etc.) between observed I/O activity (e.g., activity trace data) and workload type identifiers, ¶0064, teaches a set of observed I/O activity trace data associated with the earlier identified workloads (e.g., observed workloads 326) can be collected (i.e. aggregate) (operation 304).¶0083, teaches various workload-based resource allocation operations (e.g., workload migrations) can be generated (operation 180 3).
However, Singh remains silent on scheduling, by the processor, execution of a containerized workload on a node in the virtual computing environment based at least in part on the aggregate of the collected affinity information.
Vyas discloses scheduling, by the processor, execution of a containerized workload on a node in the virtual computing environment based at least in part on the aggregate of the collected affinity information (¶0012, i.e. Using a quantitative value to represent these hierarchical affinity relationships allows for the representation of a deployment scheme for a distributed service as an affinity distribution that is representative of the relationship between the various containers providing the distributed service, and if four containers deployed to a first node result in a certain level of performance, then four equivalent containers deployed to a second node with equivalent hardware specifications to the first node should yield a similar level of performance to the first four containers, ¶0021, i.e. a hierarchical map of a system 200 employing affinity based hierarchical container scheduling according to an example of the present disclosure. In an example, scheduler 140 may be a scheduler responsible for deploying containers (e.g., containers 152A-D, 160A-G, 260A-C, 262A-C) to nodes (e.g., nodes 112, 116, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, and 250) to provide a variety of distributed services”).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Singh’s workload affinity determination to collect a set of observed I/O activity from virtualized entities with scheduling, by the processor, execution of a containerized workload on a node in the virtual computing environment based at least in part on the aggregate of the collected affinity information of Vyas, in order to improve workload placement decisions, reduce inter-node communication overhead, decrease network latency and bandwidth consumption (Vyas).
With respect to claim 15, Singh discloses a non-transitory machine-readable medium storing instructions that, when executed by a processing resource of a computing system, cause the computing system to:
monitor a first containerized workload deployed on a node in a virtual computing environment to determine affinities between the first containerized workload and other containerized workloads in the virtual computing environment (¶0030, teaches the workload affinity determination technique 1A00 presents certain operations for implementing a computer-aided scheduling capability to allocate resources in a computing system based on observed resource usage. Specifically, workload affinity determination technique 1A00 can commence upon deploying instrumentation that serves to collect a set of observed I/O activity from virtualized entities, ¶0045, teaches workload detection agent 104 11 comprises an I/O trace monitor 106 to monitor (e.g., “trace”) a set of storage I/O activity 120 associated with various workloads (e.g., workload 174 1, . . . , workload 174 N) operating at certain virtualized entities (e.g., VE 172 111, . . . , VE 172 NMK, respectively) at distributed virtualization system 102);
collect affinity information corresponding to the determined affinities over time based on multiple user actions (¶0025, teaches a workload detection and classification model to dynamically classify workloads (i.e. affinities over time) to facilitate workload-based resource allocation in a distributed virtualization system, ¶0030, teaches workload affinity determination technique 1A00 can commence upon deploying instrumentation that serves to collect a set of observed I/O activity from virtualized entities, ¶0040, teaches the user or users interacting with the workload (e.g., user identifiers, etc.), ¶0064, teaches a set of observed I/O activity trace data associated with the earlier identified workloads (e.g., observed workloads 326) can be collected (operation 304). In the example shown, observed I/O activity trace data 128 (see FIG. 3A2) can be selected for observed workloads. The number of samples and/or period of time pertaining to the collected data can vary, ¶0067, teaches certain I/O activity in certain time periods might be more correlated to a respective workload than I/O activity in other time periods. Accordingly, only certain data from certain analysis periods are selected from trace data to be used in forming a workload detection model);
generate an aggregate of the collected affinity information (¶0046, teaches the generated workload detection and classification model 108 comprises relationships (e.g., correlations, weighted correlations, coefficients, dominance, etc.) between observed I/O activity (e.g., activity trace data) and workload type identifiers, ¶0064, teaches a set of observed I/O activity trace data associated with the earlier identified workloads (e.g., observed workloads 326) can be collected (i.e. aggregate) (operation 304).¶0083, teaches various workload-based resource allocation operations (e.g., workload migrations) can be generated (operation 180 3).
However, Singh remain silent on assign weights to each of the affinities between the first containerized workload and the other containerized workloads based on the aggregate of the collected affinity information, schedule, based at least in part on the assigned weights, execution of a container to run a second containerized workload on the node on which the first containerized workload is deployed.
Vyas disclose assign weights to each of the affinities between the first containerized workload and the other containerized workloads based on the aggregate of the collected affinity information (¶0025, teaches an affinity value based on an aggregate score may be based on a geometric mean or weighted average of the relationship between container 160A and each of containers 160B-G.); and
schedule, based at least in part on the assigned weights, execution of a container to run a second containerized workload on the node on which the first containerized workload is deployed (¶0012, i.e. Using a quantitative value to represent these hierarchical affinity relationships allows for the representation of a deployment scheme for a distributed service as an affinity distribution that is representative of the relationship between the various containers providing the distributed service, and if four containers deployed to a first node result in a certain level of performance, then four equivalent containers deployed to a second node with equivalent hardware specifications to the first node should yield a similar level of performance to the first four containers, ¶0021, i.e. a hierarchical map of a system 200 employing affinity based hierarchical container scheduling according to an example of the present disclosure. In an example, scheduler 140 may be a scheduler responsible for deploying containers (e.g., containers 152A-D, 160A-G, 260A-C, 262A-C) to nodes (e.g., nodes 112, 116, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, and 250) to provide a variety of distributed services”).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Singh’s workload affinity determination to collect a set of observed I/O activity from virtualized entities with assign weights to each of the affinities between the first containerized workload and the other containerized workloads based on the aggregate of the collected affinity information, schedule, based at least in part on the assigned weights, execution of a container to run a second containerized workload on the node on which the first containerized workload is deployed of Vyas, in order to improve workload placement decisions, reduce inter-node communication overhead, decrease network latency and bandwidth consumption (Vyas).
With respect to claims 2 and 9, Singh in view of Vyas discloses the system of claim 1, wherein the instructions further cause the system to:
assign weights to the determined affinities between the first containerized workload and the other containerized workloads based on the collected affinity information (Vyas, ¶0025, teaches an affinity value based on an aggregate score may be based on a geometric mean or weighted average of the relationship between container 160A and each of containers 160B-G.).
With respect to claims 3, 10 and 20, Singh in view of Vyas discloses the system of claim 2, wherein the instructions further cause the system to:
generate scores for nodes in the virtual computing environment based on the assigned weights (Vyas, ¶0025, teaches an affinity value based on an aggregate score may be based on a geometric mean or weighted average of the relationship between container 160A and each of containers 160B-G.); and
schedule execution of the second containerized workload on the node having the highest score (Vyas, ¶0034, teaches the scheduler 140 may determine based on the weighting criteria (i.e. highest scores) of the individual performance criteria that the third value of the performance metric is higher than the first value of the performance metric overall).
With respect to claims 4, 11 and 16, Singh in view of Vyas discloses the system of claim 1, wherein collecting the affinity information comprises:
monitoring application programming interface (API) calls between the first containerized workload and the other containerized workloads (Singh, ¶0111, teaches UI IO handler 640 and/or through any of a range of application programming interfaces (APIs), ¶0045, teaches storage I/O activity 120 can be between the virtualized entities and a storage pool 170 in the distributed virtualization system).
With respect to claims 5, 12 and 17, Singh in view of Vyas discloses the system of claim 1, wherein the node comprises a virtual computing instance or a hypervisor (Singh, ¶0054, teaches multiple VMs can operate on one physical machine (e.g., node host computer) running a single host operating system (e.g., host operating system 256 11, . . . , host operating system 256 N1), while the VMs run multiple applications on various respective guest operating systems. Such flexibility can be facilitated at least in part by a hypervisor (e.g., hypervisor 254 11, . . . , hypervisor 254 N1), which hypervisor is logically located between the various guest operating systems of the VMs and the host operating system of the physical infrastructure (e.g., node). .
With respect to claims 6, 13 and 18, Singh in view of Vyas discloses the system of claim 1, wherein the instructions further cause the system to:
schedule execution of the second containerized workload on the node based additionally on an amount of computing resources available on the node (Vyas, ¶0025, teaches containers 160A-B are both deployed on node 112, ¶0030, teaches Affinity values of the plurality of containers, including at least a first new affinity value of a first redeployed container and a second new affinity value of a second redeployed container, are measured (block 342). After redeploying the containers providing the distributed service, the scheduler 140 measures new affinity values of the redeployed containers. In an example, the new affinity values are measured with the same measurement scale as the measurements for containers 160A-G prior to redeployment, see ¶0043-¶0044).
With respect to claims 7, 14 and 19, Singh in view of Vyas discloses the system of claim 1, wherein the affinity information comprises data indicating frequencies of interactions between the first containerized workload and the other containerized workloads (Singh, ¶0032, teaches given the correlation metrics, at step 109, an affinity between an activity stream and a particular workload type can be determined and quantified (i.e. frequency of interactions), which affinity in turn can be used in making resource allocation decisions, ¶0042, teaches interactions for facilitating the foregoing workload-based resource allocation technique).
Conclusion
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/GOLAM MAHMUD/Examiner, Art Unit 2458
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