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 .
Claims 1-16 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 6 and 8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the amount of resources" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claims 6 and 8 have the same issue.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Bower, III et al. (hereinafter Bower) (US 11681353 B1).
As to claim 1, Bower teaches a system power-saving operation device comprising:
a memory storing software instructions [col. 2, lines 29-32: “some embodiments provide a computer program product comprising a non-volatile computer readable medium and non-transitory program instructions embodied therein”]; and
one or more processors configured to execute the software instructions to [col. 2, lines 32-33: “the program instructions being configured to be executable by a processor to cause the processor to perform various operations”]:
design a configuration of a DAG (disaggregated) node [composed system] included in a DAG system [a composable computer system] , based on the amount of resources required for service operation on the DAG system [col.. 3, lines 23-30: “Accordingly, a composer application having knowledge of the hardware capacity requirements of a particular workload may compose a system that includes a sufficient amount of each resource to perform the particular workload. The required compute, memory, storage and/or network resources are selected from available resource pools, and the particular workload may then be performed on the composed system.”] [Selection of required resources for the workload is generated.], so that a service can be operated at a level that meets a required performance requirement and power consumption is minimized [col. 4, lines 48-54: “Accordingly, a power cap for a memory resource, storage resource, and graphics processing resource of a composed system may be adjusted gradually to achieve a minimum power consumption value while monitoring the workload instance to verify that the composed system is meeting the performance requirements of the service level agreement associated with the workload instance.”]; and
control the configuration of the DAG node included in the DAG system based on the designed configuration [col. 3, lines 27-30: “The required compute, memory, storage and/or network resources are selected from available resource pools, and the particular workload may then be performed on the composed system.”].
As to claim 2, Bower teaches wherein the one or more processors are further configured to execute the software instructions to analyze at least one of a workload processed by the DAG system or a resource usage status of the DAG system, and estimate the amount of resources required for service operation, wherein the one or more processors design the configuration of the DAG node based on the estimated amount of resources [col.. 3, lines 23-30: “Accordingly, a composer application having knowledge of the hardware capacity requirements of a particular workload may compose a system that includes a sufficient amount of each resource to perform the particular workload. The required compute, memory, storage and/or network resources are selected from available resource pools, and the particular workload may then be performed on the composed system.”].
As to claim 3, Bower teaches wherein the one or more processors optimize the configuration of the DAG node during an operation of the DAG system, as necessary based on the designed configuration, so that power consumption is minimized [col. 4, lines 48-54: “Accordingly, a power cap for a memory resource, storage resource, and graphics processing resource of a composed system may be adjusted gradually to achieve minimum power consumption value while monitoring the workload instance to verify that the composed system is meeting the performance requirements of the service level agreement associated with the workload instance.”].
As to claim 4, Bower teaches wherein the one or more processors take as input a constraint that must be considered when determining a combination of an application instance and a device for service operation and, based on the constraint, repeatedly and stepwise perform at least one of selecting the DAG node to host the application instance or selecting the device that constitutes the DAG node, thereby deriving a configuration with low power consumption through tree search [col. 6, lines 28-38: “The rewards (power efficiency) and state (target service level agreement and current workload metrics) of the composed system in a given time interval may be input to the Reinforcement Learning algorithm and the output of the Reinforcement Learning algorithm may be an action (power capping value) that in turn affects the reward and state in the next time period. Over numerous iterations, the Reinforcement Learning algorithm learns how to best adjust a power cap to achieve power efficiency while maintaining the workload metrics in compliance with the target service level agreement.”].
As to claim 5, Bower teaches wherein the one or more processors take as input various constraints that must be considered when determining a combination of an application instance and a device for service operation, convert the constraints into a constraint set on binary variables, convert a power consumption of the DAG system into an objective function on binary variables, and determine part or all of the configuration of the DAG system by solving a mathematical optimization problem composed of the constraint set and the objective function [col. 6, lines 28-38: “The rewards (power efficiency) and state (target service level agreement and current workload metrics) of the composed system in a given time interval may be input to the Reinforcement Learning algorithm and the output of the Reinforcement Learning algorithm may be an action (power capping value) that in turn affects the reward and state in the next time period. Over numerous iterations, the Reinforcement Learning algorithm learns how to best adjust a power cap to achieve power efficiency while maintaining the workload metrics in compliance with the target service level agreement.”] [Binary is fundamental machine language in the computer. All of inputs have to be converted to binary in order to run in the computer system.].
As to claims 6 and 7, they relate to method claims comprising the similar subject matters claimed in claims 1 and 2. Therefore, they are rejected under the same reasons applied to claims 1 and 2.
As to claims 8 and 9, they relate to computer-readable medium comprising the similar subject matters claimed in claims 1 and 2. Therefore, they are rejected under the same reasons applied to claims 1 and 2.
As to claim 10, Bower teaches wherein the one or more processors optimize the configuration of the DAG node during an operation of the DAG system, as necessary based on the designed configuration, so that power consumption is minimized [col. 4, lines 48-54: “Accordingly, a power cap for a memory resource, storage resource, and graphics processing resource of a composed system may be adjusted gradually to achieve minimum power consumption value while monitoring the workload instance to verify that the composed system is meeting the performance requirements of the service level agreement associated with the workload instance.”].
as to claims 11, 13 and 15, Bower teaches wherein the one or more processors take as input a constraint that must be considered when determining a combination of an application instance and a device for service operation and, based on the constraint, repeatedly and stepwise perform at least one of selecting the DAG node to host the application instance or selecting the device that constitutes the DAG node, thereby deriving a configuration with low power consumption through tree search [col. 6, lines 28-38: “The rewards (power efficiency) and state (target service level agreement and current workload metrics) of the composed system in a given time interval may be input to the Reinforcement Learning algorithm and the output of the Reinforcement Learning algorithm may be an action (power capping value) that in turn affects the reward and state in the next time period. Over numerous iterations, the Reinforcement Learning algorithm learns how to best adjust a power cap to achieve power efficiency while maintaining the workload metrics in compliance with the target service level agreement.”].
as to claims 12, 14 and 16, Bower teaches wherein the one or more processors take as input various constraints that must be considered when determining a combination of an application instance and a device for service operation, convert the constraints into a constraint set on binary variables, convert a power consumption of the DAG system into an objective function on binary variables, and determine part or all of the configuration of the DAG system by solving a mathematical optimization problem composed of the constraint set and the objective function [col. 6, lines 28-38: “The rewards (power efficiency) and state (target service level agreement and current workload metrics) of the composed system in a given time interval may be input to the Reinforcement Learning algorithm and the output of the Reinforcement Learning algorithm may be an action (power capping value) that in turn affects the reward and state in the next time period. Over numerous iterations, the Reinforcement Learning algorithm learns how to best adjust a power cap to achieve power efficiency while maintaining the workload metrics in compliance with the target service level agreement.”] [Binary is fundamental machine language in the computer. All of inputs have to be converted to binary in order to run in the computer system.].
Conclusion
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/XUXING CHEN/Primary Examiner, Art Unit 2176
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