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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-8, 10-15 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Taherizadeh et al. (“Key Influencing Factors of the Kubernetes Auto-Scaler for Computing Intensive Microservice-Native Cloud-Based Applications”: Advances in Engineering Software, Elsevier Science, Oxford, GB, Vol. 140, 11/8/2019, pp 1-11) (hereinafter referred to as “Taherizadeh”).
Re Claims 1, 8 and 15: Taherizadeh teaches a method (and corresponding system and computer readable storage medium) comprising:
determining time-based resource utilization data for a workload executing on a
deployment (deployment of modular applications based on microservices architecture, Section 1 Introduction;
“Microservices generally are packages using container-based virtualization and deployed in the cloud.”, Section 2 Microservices architecture background;
CLPT, Section 4.2 Adaptation interval called control loop time period (CLTP));
determining a resource availability based on the resource utilization data and a
current resource allocation (current resource utilization and availability, Section 4.2 Adaptation interval called control loop time period (CLTP));
determining a severity of resource throttling of the workload based on the resource
utilization data (threshold, CPU or I/O load factors, Section 4 Proposed key influencing factors);
determining a scaling factor based at least on the severity of resource throttling (auto-scaling performed on three different workload scenarios, Section 5 Empirical evaluation); and
scaling, in response to at least the resource availability satisfying a predetermined
condition with a predetermined threshold, the deployment based on the scaling factor (Figure 3 and Section 5.1 Predictable bursting workload scenario; Figure 5 and Section 5.2 Unpredictable bursting workload scenario).
Re claim 3, 10 and 17: Taherizadeh teaches wherein the resource utilization data comprises at least one of:
historical resource utilization data for the workload; or predicted resource utilization data for the workload (Section 5.1 Predictable bursting workload scenario).
Re claim 4 and 11: Taherizadeh teaches wherein the predicted resource utilization data comprises resource utilization data determined using one or more of:
a heuristic model; or a machine-learning model (Section 4.3 learning algorithm, neural network).
Re claim 5-7, 12-14 and 18-20: Taherizadeh teaches wherein said determining the scaling factor is further
based on one or more of: a minimum resource allocation; a maximum resource allocation; a minimum slope threshold; a maximum slope threshold; a maximum single step scale-up amount;
a maximum single step scale-down amount; or a resource allocation slack amount; wherein the resource utilization data comprises one or more of: computational resource utilization data; memory resource utilization data; request latency data; storage resource utilization data; or
input/output (I/O) resource utilization data; wherein said scaling, in response the resource availability satisfying a predetermined condition with a predetermined threshold, the deployment
based on the scaling factor comprises at least one of: increasing computational resources allocatable to the deployment; increasing memory resources allocatable to the deployment;
increasing storage resources allocatable to the deployment; increasing input/output (I/O) resources allocatable to the deployment; decreasing computational resources allocatable to the deployment; decreasing memory resources allocatable to the deployment; decreasing memory resources allocatable to the deployment; or decreasing input/output (I/O) resources allocatable to the deployment (Section 4.1 “a CPU-based auto-scaling policy can be specified in a way that more container instances should be instantiated if the average CPU utilization reaches a fixed threshold such as 80%; while some container instances may be stopped if the average CPU utilization is below 80%).
Allowable Subject Matter
Claims 2, 9 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Balle et al. (USPAP 2018/0027055) teaches technologies for assigning workloads to balance multiple resource allocation objectives.
Alsop et al. (USPAP 2025/0328377) teaches methods for scheduling workloads.
Shami et al. (USPN 12,277,445) teaches predictive allocation and scheduling for distributed workload.
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/OLABODE AKINTOLA/Primary Examiner, Art Unit 3691