DETAILED ACTION
This office action is in response to amendment filed on 1/14/2026.
Claims 1, 12 and 20 are amended.
Claims 1 – 20 are pending.
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 .
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 – 7, 9 – 15 and 17 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulshreshtha et al (US 20210320853, prior art part of IDS dated 4/19/2024, hereinafter Kulshreshtha), in view of Gupta et al (US 20210112017, hereinafter Gupta), and further in view of Nidugala et al (US 20170315838. Hereinafter Nidugala).
As per claim 1, Kulshreshtha discloses: A method comprising:
establishing, by a computing system, an application performance baseline for an application based on application performance data and network telemetry data; (Kulshreshtha figure 6 and [0082]: “the method 600 may begin at step 602 in which sensors (e.g., the software sensors 112 and hardware sensors 114 of FIG. 1) capture telemetry for servers and network devices of the network (e.g., flow data, host data, process data, user data, policy data, etc.) over a first period of time to establish a baseline for application and network performance.”)
identifying, by the computing system, a correlation between a placement of a workload of the application to a first worker node of a plurality of worker nodes and the application performance data; (Kulshreshtha figure 6 and [0083]: “After collection of the telemetry, the method 600 may continue on to step 604, in which the application and network analytics platform can generate models representing applications, servers, network devices, and/or other elements of the data center.”; [0090] – [0091]: “The method 600 may continue to step 606 in which the application and network analytics platform can update one or more of the models… The modeling update(s) can include adding, removing, or moving a server resource (e.g., CPU, memory, storage, etc.), a network device resource, a data center element (e.g., a physical or virtual server, a network device, etc.), a combination of data center elements (e.g., a cluster representing an application component, a sub-network representing an application, a data center zone, a geographic region, a public cloud, etc.), or a data center element at a different level of granularity.”; [0092]: “At step 608, the application and network analytics platform can run the updated model(s) against telemetry to evaluate how the changes to the model(s) affect application and network performance.”; [0094]: “At decision point 610, if the application and network analytics platform determines that a different data center configuration reduces overall latency than the current configuration, then the application and network analytics platform can facilitate implementation of the new configuration.”.)
and based on identifying the correlation, re-scheduling, by the computing system, the workload to a second worker node of the plurality of worker nodes. (Kulshreshtha [0093] – [0094].)
Kulshreshtha did not explicitly disclose:
based on determining that application performance has degraded from the application performance baseline,
determining, by the computing system and based on the network telemetry data, that a performance requirement for the workload can be met by scheduling the workload to a second worker node having sufficient network resource availability;
However, Gupta teaches
based on determining that application performance has degraded from the application performance baseline, (Gupta [0042]: “it is determined that a migration threshold of a first non-critical application has been breached. This determination is based on second uplink health information. The second uplink health information includes parameters (e.g. jitter, latency, bandwidth usage, packet loss, etc.) of the first uplink and is regularly monitored. The second uplink health information is compared to the migration thresholds (SLAs) of each non-critical application, as well as the SLAs of each critical application, In some examples, certain noise filtering is done prior to determining that a breach of an SLA has occurred. For example, it may be required that a migration threshold be surpassed for a certain duration before the SLA is considered breached.”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Gupta into that of Kulshreshtha in order to determine that application performance has degraded from the application performance baseline. Kulshreshtha figure 6 shown a preferred embodiment of using models and modifications made to the model to reduce application latency, however, one of ordinary skill in the art can easily see that the established prior art can easily be used in reaction to a detected performance degradation (latency increase), applicants have thus merely claimed the combination of known parts in the field to achieve predictable results and is therefore rejected under 35 USC 103.
Nidugala teaches:
determining, by the computing system and based on the network telemetry data, that a performance requirement for the workload can be met by scheduling the workload to a second worker node having sufficient network resource availability; (Nidugala [0022]: “In operation, the VM migration module 104 can receive a set of criteria for operation of the VM… Some example criteria that form part of the set of criteria are server firmware-VM compatibility, ability of the server to support the VM and workloads running on the VM, availability of resources in the server for operation of the VM, compatibility of the VM with attributes of resources of the server, availability of storage area network (SAN) features to the server, compatibility of SAN type with the VM,”; [0023]: “The VM migration module 104 utilizes the set of criteria to compute a suitability score for a plurality of candidate servers.”; [0024]: “Upon computation of the suitability scores for each particular server in the plurality of candidate servers, the VM migration module 104 determines a set of servers which may be suitable for migration of the VM, i.e., to be suitable is for hosting the VM. The determination is made based on the suitability scores.”; [0025]: “the VM migration module 104 selects a destination server to which the VM is to be migrated.”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Nidugala into that of Kulshreshtha and Gupta in order to determine by the computing system and based on the network telemetry data, that a performance requirement for the workload can be met by scheduling the workload to a second worker node having sufficient network resource availability. Kulshreshtha [0092] – [0093] teaches migrating applications between servers to improve application and network performances. Nidugala [0022] – [0025] teaches the commonly known concept of first determining if a target host have enough resource availability before migrating an application would improve the migration and load balancing of the system, thus applicants have merely claimed the combination of known parts in the field to achieve better load balancing and application performance through migration and is therefore rejected under 35 USC 103.
As per claim 2, the combination of Kulshreshtha, Gupta and Nidugala further teach:
The method of claim 1, wherein network telemetry data includes at least one of: available bandwidth; network link utilization; per-hop latency among the plurality of worker nodes (Kulshreshtha [0030]); historical network telemetry data; node latency; node packet loss; or node jitter.
As per claim 3, the combination of Kulshreshtha, Gupta and Nidugala further teach:
The method of claim 1, wherein the computing system identifies the correlation based at least on one of: communication dependencies between the plurality of worker nodes; network data from service mesh telemetry; end-to-end network paths among the plurality of worker nodes; network data from the plurality of worker nodes (Kulshreshtha [0030]); network data from a probe of connections used by the plurality of worker nodes; or network data from network elements of a network that interconnects the plurality of worker nodes.
As per claim 4, the combination of Kulshreshtha, Gupta and Nidugala further teach:
The method of claim 1, further comprising: obtaining, by the computing system, data regarding network performance of the plurality of worker nodes of the network; identifying, by the computing system, worker nodes of the plurality of worker nodes that are executing any workload of the application; (Kulshreshtha figure 6 and [0082].)
and determining, by the computing system and based on the data regarding network performance and the identified worker nodes, a degraded network performance of the application caused by the network performance of the identified worker nodes, wherein the re-scheduling is based on the determined degraded network performance of the application. (Gupta [0042].)
As per claim 5, the combination of Kulshreshtha, Gupta and Nidugala further teach:
The method of claim 4, further comprising: establishing, by the computing system, a network performance baseline for the plurality of worker nodes, wherein identifying the correlation is based at least in part on the data regarding network performance and the network performance baseline. (Kulshreshtha figure 6 and [0082].)
As per claim 6, the combination of Kulshreshtha, Gupta and Nidugala further teach:
The method of claim 4, further comprising: obtaining, by the computing system, data regarding intermediate connections between the plurality of worker nodes; and determining, by the computing system and based on the intermediate connections and based on the data regarding network performance of the plurality of worker nodes, a degraded network performance of the application caused by network performance of the intermediate connections between the plurality of worker nodes, and wherein the re-scheduling is based on the determined degraded network performance of the application caused by the network performance of the intermediate connections. (Kulshreshtha [0082] and Gupta [0042].)
As per claim 7, the combination of Kulshreshtha, Gupta and Nidugala further teach:
The method of claim 1, further comprising: obtaining, by the computing system, quality of service (QoS) requirements for the workload of the application; and determining, by the computing system, that the QoS requirements of the workload are not met, wherein re-scheduling includes re-scheduling the workload based on the QoS requirements. (Gupta [0042].)
As per claim 9, the combination of Kulshreshtha, Gupta and Nidugala further teach:
The method of claim 1, further comprising: determining, by the computing system, that a non-critical workload of the application is deployed to a worker node, of the plurality of worker nodes, having degraded network performance; determining, by the computing system, the absence of a correlation between the placement of the non-critical workload and the application performance data; and keeping, by the computing system, the non-critical workload as scheduled to the worker node with degraded network performance. (Kulshreshtha figure 6, step 610, no branch.)
As per claim 10, the combination of Kulshreshtha, Gupta and Nidugala further teach:
The method of claim 1, wherein the correlation is a first correlation, and comprising: obtaining, by the computing system, indicators of dependencies between a plurality of resources in the network and event dependencies between a plurality of network events and one or more of the plurality of resources; receiving, by the computing system, an indication of a fault in the network; based on identifying that application performance has degraded from the application performance baseline, identifying, by the computing system, a second correlation between the fault, a placement of a second workload to a third worker node, and the application performance data; and wherein re-scheduling comprises re-scheduling based on the second correlation. (Kulshreshtha figure 6, [0083] and [0091] – [0094])
As per claim 11, the combination of Kulshreshtha, Gupta and Nidugala further teach:
The method of claim 1, further comprising: by the computing system, based on the re-scheduling and determining that one or more Quality of Service (QoS) requirements for the application is not met by a wide area network (WAN) link that interconnects at least two of the plurality of worker nodes, the at least two of the plurality of worker nodes including the first worker node, outputting a request to a software- defined networking in a WAN (SD-WAN) controller to configure an SD-WAN to transport traffic associated with the first worker node using a WAN link that satisfies the QoS requirements for the application. (Gupta [0057] and [0061] – [0062])
As per claim 12, it is the system variant of claim 1 and is therefore rejected under the same rationale.
As per claim 13, it is the system variant of claim 4 and is therefore rejected under the same rationale.
As per claim 14, it is the system variant of claim 5 and is therefore rejected under the same rationale.
As per claim 15, it is the system variant of claim 6 and is therefore rejected under the same rationale.
As per claim 16, it is the system variant of claim 7 and is therefore rejected under the same rationale.
As per claim 18, it is the system variant of claim 10 and is therefore rejected under the same rationale.
As per claim 19, it is the system variant of claim 11 and is therefore rejected under the same rationale.
As per claim 20, it is the non-transitory computer-readable storage media variant of claim 1 and is therefore rejected under the same rationale.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulshreshtha, Gupta and Nidugala, in view of Horikawa (US 20050010608).
As per claim 8, the combination of Kulshreshtha, Gupta and Nidugala did not teach:
The method of claim 1, wherein re-scheduling the workload comprises re-scheduling the workload based on determining the workload of the application is a critical workload.
However, Horikawa teaches:
The method of claim 1, wherein re-scheduling the workload comprises re-scheduling the workload based on determining the workload of the application is a critical workload. (Horikawa [0086] – [0087])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of into that of Kulshreshtha, Gupta and Nidugala in order to reschedule the workload comprises re-scheduling the workload based on determining the workload of the application is a critical workload. Horikawa has shown the claimed limitations are mere, applicants have thus merely claimed the combination of known parts in the field to achieve predictable results and is therefore rejected under 35 USC 103.
As per claim 17, it is the system variant of claim 8 and is therefore rejected under the same rationale.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 – 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CHARLES M SWIFT/Primary Examiner, Art Unit 2196