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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over “Migration Modeling and Learning Algorithms for Containers in Fog Computing” by Zhiqing Tang et al. (TANG) in view of HE et al. (Publication 2022/0206873)
As to claim 1, TANG teaches a method, comprising: obtaining information characterizing a power consumption of respective ones of a plurality of containers (pg. 713-714, Section 2.1 System Model); applying the information characterizing the power consumption of the respective ones of the plurality of containers to at least one reinforcement learning model that determines at least one reward value for moving one or more containers associated with a given node, of the plurality of nodes, to at least one different node of the at least one cluster (pg. 716-717, particularly sections – Container Migration Algorithms in Fog Computing Architecture, Reinforcement Learning Settings, pg. 717-719, particularly sections - Selection Policy and Allocation Policy); and automatically controlling a movement of at least one of the one or more containers to the at least one different node based at least in part on the at least one reward value (pg. 716-717, particularly sections – Container Migration Algorithms in Fog Computing Architecture, Reinforcement Learning Settings, pg. 717-719, particularly sections - Selection Policy and Allocation Policy).
However, TANG does not explicitly teach that the environment for container migration is based on the plurality of containers of at least cluster of a containerized environment wherein the at least one cluster comprises a plurality of nodes; wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
HE teaches a known environment for performing container balancing / migration wherein the environment for container migration is based on the plurality of containers of at least cluster of a containerized environment wherein the at least one cluster comprises a plurality of nodes ([0020-0021, 0026]); wherein the method is performed by at least one processing device comprising a processor coupled to a memory ([0026, 0032, 0075, 0103-0106]).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing of the claimed invention to incorporate the teachings of TANG in the well known environment of HE in order to pre-emptive container load-balancing, auto-scaling and placement in a cloud / edge environment ([0016-0017]).
As to claim 2, TANG teaches obtaining information characterizing a power consumption of respective ones of the plurality of nodes; applying the information characterizing the power consumption of the respective ones of the plurality of nodes to the at least one reinforcement learning model that determines at least one second reward value for moving the one or more containers associated with the given node to the at least one different node of the at least one cluster; and automatically selecting the at least one different node based at least in part on the at least one second reward value (pg. 716-717, particularly sections – Container Migration Algorithms in Fog Computing Architecture, Reinforcement Learning Settings, pg. 717-719, particularly sections - Selection Policy and Allocation Policy Further note in section 3.1, pg. 716-719, that the Reward is recalculating over different time periods that updates the Q-learning algorithm and subsequent action that is taken and thus a second reward is calculated and dictated for determining the action to be taken for moving a container / determining a node for placement).
HE teaches a known environment for performing container balancing / migration wherein the environment for container migration is based on the plurality of containers of at least cluster of a containerized environment wherein the at least one cluster comprises a plurality of nodes ([0020-0021, 0026]). Refer to claim 1 for the motivation to combine.
As to claim 3, TANG teaches the power consumption of the respective ones of the plurality of containers is determined by evaluating a utilization of one or more resources of the respective ones of the plurality of containers for a designated time interval (pg. 717-719, particularly sections - Selection Policy and Allocation Policy).
As to claim 4, TANG teaches the utilization of the one or more resources of the given container comprises a utilization of at least one of a processing resource, a storage resource, a memory resource and a network resource (pg. 717-719, particularly sections - Selection Policy and Allocation Policy). Further note on pg. 714, System model section - that it is known when determining the adequacy for handling containers whether sufficient memory, storage, network capacity for the containters exist in the node.
As to claim 5, TANG teaches determining a power consumption of a given node by aggregating a power consumption of a plurality of containers associated with the given node (pg. 717-719, particularly sections - Allocation Policy which when determining the node to migrate a container to, calculates total estimated delay, power consumption and migration costs to that node).
As to claim 6, TANG teaches initiating a retraining of the at least one reinforcement learning model according to a designated schedule (pg. 716-717, particularly sections – Reinforcement Learning Settings which for each T, collecting of system state and recalculating the reward and choosing an action and thereby this teaches retraining the reinforcement learning model).
As to claim 7, TANG teaches the controlling the movement of the at least one container to the at least one different node is performed in accordance with at least one designated container movement policy (pg. 717-719, particularly sections - Selection Policy and Allocation Policy).
As to claim 8, TANG teaches the at least one reinforcement learning model comprises at least one container movement selection reinforcement learning model and at least one destination node selection reinforcement learning model (Examiner Note, TANG algorithm teaches performing two distinct policy algorithms in container migration (Selection Policy, which determines which container to migrate and Allocation Policy which determines which target node to migrated the selected container to) – see pg. 717-719. It would be obvious to one of ordinary skill in the art before the effective filing of the claimed invention this constitutes the modeling as claimed.)
As to claims 9-14, reference is made to an apparatus that corresponds to the method of claims 1-3 and 6-8 and is therefore met by the rejection of claims 1-3 and 6-8 above.
As to claims 15-20, reference is made to an article of manufacture, non-transitory processor readable storage medium, that corresponds to the method of claims 1-3 and 6-8 and is therefore met by the rejection of claims 1-3 and 6-8 above.
Conclusion
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/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
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