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 1-25 are pending for examination.
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 1-25 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 language in the following claims is not clearly understood:
As per claim 1, line 15, it is unclear whether “performance” is referring to “a computing performance” in line 13 (i.e. consistent term should be used with “the” or “said”)
As per claims 8, 15, 21 and 15, they have the same deficiency as claim 1 above. Appropriate correction is required.
As per claims 2-7, 9-14, 16-20, 22-24, they depend from rejected claims and do not resolve the deficiencies thereof and are therefore rejected for at least the same reasons.
Claim Rejections - 35 USC § 103
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 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, 4-6, 8, 11-13, 15, 18-19 are is/are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee et al. US Pub 2023/0129548 (hereafter Mukherjee) in view of Bohacek et al. US Pub 2019/0286491 (hereafter Bohacek).
As per claim 1, Mukherjee teaches the invention substantially as claimed including a method for deploying workloads in a computing system based on energy efficiency, the method comprising: classifying each of the plurality of compute nodes into one of a plurality of energy efficiency groups based on the energy efficiency metric of each of the plurality of compute nodes (para[0026, 0039-0042, 0050], FIG. 2 and 5, as the node devices perform workloads, power consumption and performance (operating information) associated with each node are determined and stored in a database, and the nodes are grouped into different node groups based on their performance and power consumption);
creating a partition of nodes from the plurality of compute nodes, wherein the partition includes one compute node selected from each of the plurality of energy efficiency groups; deploying a replica of a workload to each of the plurality of compute nodes in the partition (para[0031, 0039-0042], deploy a respective workload on at least one node device in each of the homogenous node groups);
monitoring an energy consumption and a computing performance of each of the plurality of compute nodes in the partition during a probing period (para[0031, 0039-0041, 0048-0050, 0056], power consumption and performance of each nodes are monitored and the system generates a workload performance efficiency ranking of the nodes);
identifying, based on the energy consumption and performance, a selected energy efficiency group from the plurality of energy efficiency groups, and deploying the workload to one or more of the plurality of compute nodes in the selected energy efficiency group in the selected energy efficiency group (para[0031, 0039-0041, 0048-0050, 0054], nodes of a selected group is identified based on the workload performance efficiency ranking and the workloads are deployed to the nodes of a group which will perform in the most power-efficient manner).
Mukherjee does not explicitly teach obtaining an energy efficiency metric for each of a plurality of compute nodes in the cloud computing environment.
However, Bohacek teaches obtaining an energy efficiency metric for each of a plurality of compute nodes in the cloud computing environment (para[0022, 0029-0030, 0055-0058], characteristics associated with each compute node in a cloud computer system, including information related to performance and energy efficiency is obtained and stored).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bohacek’s teaching to Mukherjee’s invention in order to provide a method for analyzing and associating elements of a computer system by shared characteristics, including node’s energy efficiency and relative performance, to improve the efficiency and reduce errors when migrating to a new infrastructure (para[0017, 0055]).
As per claim 4, Mukherjee teaches wherein the plurality of energy efficiency groups includes at least three groups para[0038-0039], node groups include high performance/ high power consumption, high performance/low power consumption, low performance/high power consumption and low performance/low power consumption).
As per claim 5, Mukherjee teaches wherein the energy efficiency metric for each of the plurality of compute nodes is obtained from each of the plurality of compute nodes by querying each of the plurality of compute nodes (para[0035, 0038], retrieve a vendor specification/performance metric which provides power consumption/performance information for each of the nodes from node device efficiency management database).
As per claim 6, Bohacek teaches wherein a duration of the probing period is set by an administrator of the cloud computing system (para[0035-0036, 0046], user is able to define a specific set of grouping criteria using UI, and the user select a period of time to monitor performance of the nodes).
As per claim 8, it is a computing system claim of claim 1 above, thus it is rejected for the same rationale.
As per claim 11, it is a computing system claim of claim 4 above, thus it is rejected for the same rationale.
As per claim 12, it is a computing system claim of claim 5 above, thus it is rejected for the same rationale.
As per claim 13, it is a computing system claim of claim 6 above, thus it is rejected for the same rationale.
As per claim 15, it is a computer program product claim of claim 1 above, thus it is rejected for the same rationale.
As per claim 18, it is a computer program product claim of claim 5 above, thus it is rejected for the same rationale.
As per claim 19, it is a computer program product claim of claim 6 above, thus it is rejected for the same rationale.
Claim(s) 2-3, 9-10, 16-17 are is/are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of Bohacek as applied to claim 1 above, and further in view of Park et al. US Pub 2015/0286262 (hereafter Park).
As per claim 2, Mukherjee and Bohacek teach the method of claim 1, but they do not explicitly teach wherein the energy efficiency metric includes the computing performance per unit of consumed energy.
However, Park teaches the energy efficiency metric includes the computing performance per unit of consumed energy (para[0044], energy efficiency is represented by instruction per second per mW power consumption).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Park’s teaching to Mukherjee and Bohacek’s invention in order to optimize the overall processing efficiency and performance of the SoC by providing energy efficiency aware thermal mitigation method which compares processing components and identifying most and least efficient processing components and reduces power consumptions of the least energy efficient processing component by adjusting the power supply voltage and frequency (para[0007]).
As per claim 3, Mukherjee and Bohacek teach the method of claim 1, but they do not explicitly teach wherein the computing performance is measured using one or more of a number of instructions executed per second, a number of disk operations performed per second, and a number of network operations performed per second by the compute node.
However, Park teaches the computing performance is measured using one or more of a number of instructions executed per second, a number of disk operations performed per second, and a number of network operations performed per second by the compute node (para[0044], energy efficiency is represented by instruction per second per mW power consumption).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Park’s teaching to Mukherjee and Bohacek’s invention in order to optimize the overall processing efficiency and performance of the SoC by providing energy efficiency aware thermal mitigation method which compares processing components and identifying most and least efficient processing components and reduces power consumptions of the least energy efficient processing component by adjusting the power supply voltage and frequency (para[0007]).
As per claim 9, it is a computing system claim of claim 2 above, thus it is rejected for the same rationale.
As per claim 10, it is a computing system claim of claim 3 above, thus it is rejected for the same rationale.
As per claim 16, it is a computer program product claim of claim 2 above, thus it is rejected for the same rationale.
As per claim 17, it is a computer program product claim of claim 3 above, thus it is rejected for the same rationale.
Claim(s) 7, 14, 20-21, 23-25 are is/are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of Bohacek as applied to claim 1 above, and further in view of Ocon Cardenas et al. US Pub 2023/0118846 (hereafter Ocon).
As per claim 7, Mukherjee and Bohacek teach the method of claim 1, and Mukherjee teaches selecting the one or more of the plurality of compute nodes in the selected energy efficiency group (para[0031, 0039-0041, 0048-0050, 0054], nodes of a selected group is identified based on the workload performance efficiency ranking and the workloads are deployed to the nodes of a group which will perform in the most power-efficient manner).
Mukherjee and Bohacek do not explicitly teach further comprising: identifying a resource availability of each of the plurality of compute nodes in the selected energy efficiency group; and selecting the one or more of the plurality of compute nodes based on the resource availability of each of the plurality of compute nodes.
However, Ocon teaches identifying a resource availability of each of the plurality of compute nodes in the selected energy efficiency group; and selecting the one or more of the plurality of compute nodes in the selected energy efficiency group based on the resource availability of each of the plurality of compute nodes in the selected energy efficiency group (para[0012, 0025, 0038-0039, 0050] scheduler is selecting a node to deploy a workload based on the availability on the node and the resource requirements of the workload, and the resource usage (performance) of the nodes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ocon’s teaching to Mukherjee and Bohacek’s invention in order to maximize/optimize usage of resources, to increase system wide performance and to avoid limiting resource availability by scheduling and utilizing unused resources without reserving an excessive amount of resources for the workload for the future, which provides enhanced results to clients of the processing infrastructure (para[0001-0002, 0011]).
As per claim 14, it is a computing system claim of claim 7 above, thus it is rejected for the same rationale.
As per claim 20, it is a computer program product claim of claim 7 above, thus it is rejected for the same rationale.
As per claim 21, Mukherjee teaches the invention substantially as claimed including a method for deploying workloads in a cloud computing system based on energy efficiency, the method comprising: classifying each of the plurality of compute nodes into one of a plurality of energy efficiency groups based on the energy efficiency metric of each of the plurality of compute nodes (para[0026, 0039-0042, 0050], FIG. 2 and 5, as the node devices perform workloads, power consumption and performance (operating information) associated with each node are determined and stored in a database, and the nodes are grouped into different node groups based on their performance and power consumption);
creating a partition of nodes from the plurality of compute nodes, wherein the partition includes one compute node selected from each of the plurality of energy efficiency groups; deploying a replica of a workload to each of the plurality of compute nodes in the partition (para[0031, 0039-0042], deploy a respective workload on at least one node device in each of the homogenous node groups);
monitoring an energy consumption and a computing performance of each of the plurality of compute nodes in the partition during a probing period (para[0031, 0039-0041, 0048-0050, 0056], power consumption and performance of each nodes are monitored and the system generates a workload performance efficiency ranking of the nodes);
identifying, based on the energy consumption and performance, a selected energy efficiency group from the plurality of energy efficiency groups; and deploying the workload to one or more of the plurality of compute nodes in the selected energy efficiency group (para[0031, 0039-0041, 0048-0050, 0054], nodes of a selected group is identified based on the workload performance efficiency ranking and the workloads are deployed to the nodes of a group which will perform in the most power-efficient manner);
the one or more of the plurality of compute nodes in the selected energy efficiency group selected (para[0031, 0039-0041, 0048-0050, 0054], nodes of a selected group is identified based on the workload performance efficiency ranking and the workloads are deployed to the nodes of a group which will perform in the most power-efficient manner).
Mukherjee does not explicitly teach obtaining an energy efficiency metric and a resource availability for each of a plurality of compute nodes in the cloud computing environment.
However, Bohacek teaches obtaining an energy efficiency metric and a resource availability for each of a plurality of compute nodes in the cloud computing environment (para[0022, 0029-0030, 0055-0058], characteristics associated with each compute node in a cloud computer system, including information related to performance and energy efficiency is obtained and stored).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bohacek’s teaching to Mukherjee’s invention in order to provide a method for analyzing and associating elements of a computer system by shared characteristics, including node’s energy efficiency and relative performance, to improve the efficiency and reduce errors when migrating to a new infrastructure (para[0017, 0055]).
Mukherjee and Bohacek do not explicitly teach the one or more of the plurality of compute nodes in the selected energy efficiency group selected based on the resource availability of each of the plurality of compute nodes in the selected energy efficiency group.
However, Ocon teaches the one or more of the plurality of compute nodes selected based on the resource availability of each of the plurality of compute nodes (para[0012, 0025, 0038-0039, 0050] scheduler is selecting a node to deploy a workload based on the availability on the node and the resource requirements of the workload, and the resource usage (performance) of the nodes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ocon’s teaching to Mukherjee and Bohacek’s invention in order to maximize/optimize usage of resources, to increase system wide performance and to avoid limiting resource availability by scheduling and utilizing unused resources without reserving an excessive amount of resources for the workload for the future, which provides enhanced results to clients of the processing infrastructure (para[0001-0002, 0011]).
As per claim 23, Mukherjee, Bohacek and Ocon teach the method of claim 21, and Bohacek teaches wherein a duration of the probing period is set by an administrator of the cloud computing system (para[0035-0036, 0046], user is able to define a specific set of grouping criteria using UI, and the user select a period of time to monitor performance of the nodes).
As per claim 24, Mukherjee teaches wherein the energy efficiency metric for each of the plurality of compute nodes is obtained from each of the plurality of compute nodes by querying each of the plurality of compute nodes (para[0035, 0038], retrieve a vendor specification/performance metric which provides power consumption/performance information for each of the nodes from node device efficiency management database).
As per claim 25, Mukherjee teaches the invention substantially as claimed including a method for deploying a workload in a cloud computing system based on energy efficiency, the method comprising: obtaining resource requirements of the workload (para[0031, 0045, 0056], workload performance requirements are obtained);
classifying each of the plurality of compute nodes into one of a plurality of energy efficiency groups based on the energy efficiency metric of each of the plurality of compute nodes (para[0026, 0039-0042, 0050], FIG. 2 and 5, as the node devices perform workloads, power consumption and performance (operating information) associated with each node are determined and stored in a database, and the nodes are grouped into different node groups based on their performance and power consumption);
creating a partition of nodes from the plurality of compute nodes, wherein the partition includes one compute node selected from each of the plurality of energy efficiency groups; deploying a replica of the workload to each of the plurality of compute nodes in the partition (para[0031, 0039-0042], deploy a respective workload on at least one node device in each of the homogenous node groups);
monitoring an energy consumption and a computing performance of each of the plurality of compute nodes in the partition during a probing period (para[0031, 0039-0041, 0048-0050, 0056], power consumption and performance of each nodes are monitored and the system generates a workload performance efficiency ranking of the nodes);
identifying, based on the energy consumption and performance, a selected energy efficiency group from the plurality of energy efficiency groups; and deploying the workload to one or more of the plurality of compute nodes in the selected energy efficiency group (para[0031, 0039-0041, 0048-0050, 0054], nodes of a selected group is identified based on the workload performance efficiency ranking and the workloads are deployed to the nodes of a group which will perform in the most power-efficient manner).
Mukherjee does not explicitly teach identifying a plurality of compute nodes in the cloud computing system that have a resource availability sufficient for the resource requirements of the workload; obtaining an energy efficiency metric for each of the plurality of compute nodes;
However, Bohacek teaches obtaining an energy efficiency metric for each of the plurality of compute nodes (para[0022, 0029-0030, 0055-0058], characteristics associated with each compute node in a cloud computer system, including information related to performance and energy efficiency is obtained and stored).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bohacek’s teaching to Mukherjee’s invention in order to provide a method for analyzing and associating elements of a computer system by shared characteristics, including node’s energy efficiency and relative performance, to improve the efficiency and reduce errors when migrating to a new infrastructure (para[0017, 0055]).
Mukherjee and Bohacek do not explicitly teach identifying a plurality of compute nodes in the cloud computing system that have a resource availability sufficient for the resource requirements of the workload.
However, Ocon teaches identifying a plurality of compute nodes in the cloud computing system that have a resource availability sufficient for the resource requirements of the workload (para[0012, 0025, 0038-0039, 0050] scheduler is selecting a node to deploy a workload based on the availability on the node and the resource requirements of the workload, and the resource usage (performance) of the nodes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ocon’s teaching to Mukherjee and Bohacek’s invention in order to maximize/optimize usage of resources, to increase system wide performance and to avoid limiting resource availability by scheduling and utilizing unused resources without reserving an excessive amount of resources for the workload for the future, which provides enhanced results to clients of the processing infrastructure (para[0001-0002, 0011]).
Claim(s) 22 are is/are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of Bohacek and Ocon in view of claim 21, and further in view of Park et al. US Pub 2015/0286262 (hereafter Park).
As per claim 22, Mukherjee, Bohacek and Ocon teach the method of claim 21, but they do not explicitly teach wherein the computing performance is measured using one or more of a number of instructions executed per second, a number of disk operations performed per second, and a number of network operations performed per second by the compute node.
However, Park teaches the computing performance is measured using one or more of a number of instructions executed per second, a number of disk operations performed per second, and a number of network operations performed per second by the compute node. (para[0044], energy efficiency is represented by instruction per second per mW power consumption).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Park’s teaching to Mukherjee, Bohacek and Ocon’s invention in order to optimize the overall processing efficiency and performance of the SoC by providing energy efficiency aware thermal mitigation method which compares processing components and identifying most and least efficient processing components and reduces power consumptions of the least energy efficient processing component by adjusting the power supply voltage and frequency (para[0007]).
Conclusion
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/TAMMY E LEE/Primary Examiner, Art Unit 2195