DETAILED ACTION
This action is responsive to the communication filed on 8/15/2025. Claims 1-20 are pending and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/15/2025 has been entered.
Claim Objections
Claims 1-20 is objected to because of the following informalities:
With respect to claims 1, 10, and 16, the examiner suggests amending “the plurality of the storage units” (e.g. claim 1, lines 10-11) as “the plurality of [[the]] storage units”.
With respect to claim 4, the examiner suggests amending “…whether the performance metric has been reached the threshold.”, as “…whether the performance metric has [[been]] reached the threshold.”
With respect to claim 8, the examiner suggests amending “storage system-based on the performance data…” (lines 4-5) as “storage [[system-based]] system based on the performance data…”
Rest of the claims as listed are objected for being dependent on an objected claim.
Appropriate correction is required.
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 11, 13-15, and 18-20 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.
Claims 11, 13-15, and 19-20 recite ‘the plurality of volumes”, but there is insufficient antecedent basis for this term.
Claim 14 recites “the optimal utilization range.” While the claim recites “a utilization range”, there is insufficient antecedent basis for the optimal utilization range.
Claims 18 and 19 respectively recite “the selected threshold value” and “the threshold value”, but there is insufficient antecedent basis for these terms.
Claim 12 is rejected for being dependent on a rejected claim.
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 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.
Claims 1-5, 8-14, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Du et al. (US 20190253490 A1) in view of Yahalom et al. (US 20090172666 A1).
As per claim 1,
1. A system, comprising: a memory to store one or more instructions; and one or more processors to execute the one or more instructions to: [Du teaches cluster system comprising cluster nodes and a processor executing application program stored in memory (para. 3, 176-178)] forecast performance metrics for a plurality of storage unit subsets of a storage system based on performance data for the storage system, wherein the performance metrics indicate a saturation of a capacity related to the plurality of storage unit subsets; [Du teaches predicting resource load (performance metric) of the cluster nodes, comprising applications, for a preset time period based on performance data, the resource load and performance data reflecting usage of resources including computing and storage resources (para. 7-8, 14, 20-21, 48, 56-59, 73-80, 104-109); Du teaches distinguishing a cluster node with the heaviest resource load from the rest of the cluster nodes, wherein a migration of an application from the heaviest cluster node to another cluster node is simulated (and may be performed) for bringing resource load of the heaviest cluster node closer to an average resource load of the system (para. 114-124; 27-28, 48, 73-84, 104-108, 111, 113, 115; fig. 2, 2A and associated paragraphs)]
Du does not explicitly disclose, but Yahalom discloses:
rank a plurality of storage units within the plurality of storage unit subsets based on the performance metrics to determine a ranking order of the plurality of the storage units to modify the storage system; and modify the storage system in order of the ranking. [Du as shown above teaches simulating migration of an application from a cluster node with the heaviest resource load to another cluster node for bringing the resource load of the heaviest cluster node closer to an average resource node (see Du above); Yahalom discloses migration of a virtual machine from a storage component having a high storage utilization ranking to another storage component having a low storage utilization ranking for reducing storage utilization of the storage component having the high storage utilization ranking, wherein Yahalom uses respective thresholds for determining storage utilization rankings of storage components and for determining I/O traffic load generation rankings of the virtual machines (e.g. high or low ranking), and where Yahalom identifies a high ranking virtual machine in a high ranking storage component for migration to a low ranking storage component (para. 44-45, 71-80; figs. 3A-C, 4, and associated paragraphs); it would have been obvious for one of ordinary skill in the arts, provided with the disclosures by Du and Yahalom, both directed towards reducing load of a heavily utilized node, to provide for a combination comprising ranking the applications and the cluster nodes based on resource loads using respective thresholds and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node. Doing so would allow for improved efficiency in determining migration destinations as well as applications to be subject to migration.
Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 2, Du in view of Yahalom teaches claim 1 as shown above and further teaches:
2. The system of claim 1, wherein the ranking is based on a quantity that a performance metric exceeds a threshold. [Du in view of Yahalom as shown above teaches thresholds used for providing rankings (e.g. high ranking for resource load exceeding the threshold) (Yahalom: para. 45, 71-80; figs. 3A-C, 4, and associated paragraphs)]
Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 3, Du in view of Yahalom teaches claim 1 as shown above and further teaches:
3. The system of claim 1, wherein the corresponding performance metrics are based on at least one of a storage capacity or a performance capacity for the plurality of storage unit subsets. [Du as shown above teaches predicting resource load (performance metric) of cluster nodes for a preset time period based on performance data, the resource load and performance data reflecting usage of resources including computing and storage resources (para. 7-8, 14, 20-21, 56-59, 73-80, 104-109)]
As per claim 4, Du in view of Yahalom teaches claim 2 as shown above and further teaches:
4. The system of claim 2, wherein the modification is further based on a proactive determination as to whether the performance metric has been reached the threshold.
[Du in view of Yahalom as shown above teaches using thresholds for classifying rankings of applications as well as the cluster nodes in association with performing a migration of an application, prior to the migration (see claims 1, 2 above; Yahalom: para. 45, 71-80; figs. 3A-C, 4, and associated paragraphs), wherein an application or cluster exceeding a respective threshold necessarily has reached said threshold.]
Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 5, Du in view of Yahalom teaches claim 1 as shown above and further teaches:
5. The system of claim 1, wherein ranking the plurality of storage units comprises: evaluating a combination of storage capacity and performance capacity caused by functioning of the plurality of storage unit subsets, and providing the corresponding rankings to the plurality of storage unit subsets based on an aggregation of storage capacity and performance capacity. [Du in view of Yahalom as shown above teaches predicting resource load (performance metric) of cluster nodes for a preset time period based on performance data, the resource load and performance data reflecting usage of resources including computing and storage resources (Du: para. 7-8, 14, 20-21, 56-59, 73-80, 104-109) as well as providing rankings to the cluster nodes based on resource loads (see claim 1 above; Yahalom: para. 45, 71-80; figs. 3A-C, 4, and associated paragraphs)]
Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 8, Du in view of Yahalom teaches claim 1 as shown above and further teaches:
8. The system of claim 1, wherein the one or more processors further execute the one or more instructions to forecast a first performance metric for a first storage unit subset of the storage system-based on the performance data for the storage system; forecast a second performance metric for a second storage unit subset of the storage system; modify the storage system relative to the second storage unit subset based on a comparison of a ranking of the second performance metric against the first performance metric.
[Du in view of Yahalom as shown above teaches ranking cluster nodes as high ranking or low ranking cluster nodes based on their resource loads at a predetermined time period using a threshold and migrating an application from a high ranking cluster node to a low ranking cluster node (see claim 1 above; Du: para. 7-8, 14, 20-21, 56-59, 73-80, 104-109, 124; Yahalom: para. 45, 71-80; figs. 3A-C, 4, and associated paragraphs))]
Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 9, Du in view of Yahalom teaches claim 8 as shown above and further teaches:
9. The system of claim 8, wherein the comparison of the ranking comprises determining a synthetic indicator that comprises historical factors that are historically indicative of a capacity of the first storage unit subset or the second storage unit subset. [Du teaches predicting the resource loads for the cluster nodes for the preset time period using historical performance data (Du: para. 73-80, 104-108)]
As per claim 10,
A computer-implemented method, comprising: [Du teaches cluster system comprising cluster nodes and a processor executing application program stored in memory (para. 3, 176-178)] forecasting performance metrics for a plurality of storage unit subsets of a storage system based on performance data for the storage system, wherein the performance metrics indicate a saturation of a capacity related to the plurality of storage unit subsets; [Du teaches predicting resource load (performance metric) of cluster nodes, comprising applications, for a preset time period based on performance data, the resource load and performance data reflecting usage of resources including computing and storage resources (para. 7-8, 14, 20-21, 48, 56-59, 73-80, 104-109); Du teaches distinguishing a cluster node with the heaviest resource load from the rest of the cluster nodes, wherein a migration of an application from the heaviest cluster node to another cluster node is simulated (and may be performed) for bringing resource load of the heaviest cluster node closer to an average resource load of the system (para. 114-124; 27-28, 48, 73-84, 104-108, 111, 113, 115; fig. 2, 2A and associated paragraphs)]
Du does not explicitly disclose, but Yahalom discloses:
ranking a plurality of storage units in the plurality of storage unit subsets based on the performance metrics to determine a ranking order of the plurality of the storage units to modify the storage system; and modifying the storage system in order of the ranking. [Du as shown above teaches simulating migration of an application from a cluster node with the heaviest resource load to another cluster node for bringing the resource load of the heaviest cluster node closer to an average resource node (see Du above); Yahalom discloses migration of a virtual machine from a storage component having a high storage utilization ranking to another storage component having a low storage utilization ranking for reducing storage utilization of the storage component having the high storage utilization ranking, wherein Yahalom uses respective thresholds for determining storage utilization rankings of storage components and for determining I/O traffic load generation rankings of the virtual machines (e.g. high or low ranking), and where Yahalom identifies a high ranking virtual machine in a high ranking storage component for migration to a low ranking storage component (para. 44-45, 71-80; figs. 3A-C, 4, and associated paragraphs); it would have been obvious for one of ordinary skill in the arts, provided with the disclosures by Du and Yahalom, both directed towards reducing load of a heavily utilized node, to provide for a combination comprising ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on high ranking cluster node to a low ranking cluster node. Doing so would allow for improved efficiency in determining migration destinations as well as applications to be subject to migration.Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 11, Du in view of Yahalom teaches claim 10 as shown above and further teaches:
11. The computer-implemented method of claim 10, wherein the ranking is based on a severity of the corresponding performance metrics of the plurality of volumes, [Du in view of Yahalom as shown above teaches predicting resource loads reflecting usage of resources including computing and storage resources and also teaches providing rankings to the cluster nodes based on resource loads (Du: para. 7-8, 14, 20-21, 56-59, 73-80, 104-109; Yahalom: para. 45, 71-80; figs. 3A-C, 4, and associated paragraphs)] or based on an altered ranking determined by entity feedback. [Where the claim recites the ranking being based on one of a severity of the corresponding performance metrics of the plurality of volumes or an altered ranking, based on the scope of the claim, the combination of the prior arts providing the ranking being based on a severity of the corresponding performance metrics of the plurality of volumes as shown above is not required to additionally provide for an altered ranking as recited in the claim.]
Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 12, Du in view of Yahalom teaches claim 11 as shown above and further teaches:
12. The computer-implemented method of claim 11, wherein the altered ranking comprises: receiving second performance data for the storage system indicating a saturation of an updated capacity related to the plurality of storage unit subsets; re-ranking the plurality of storage units in the plurality of storage unit subsets based on the second performance metrics to determine the order in which to modify data at the storage units. [Where claim 11 above recites the ranking being based on one of a severity of the corresponding performance metrics of the plurality of volumes or an altered ranking, based on the scope of the claim, the combination of the prior arts providing the ranking being based on a severity of the corresponding performance metrics of the plurality of volumes as shown above in claim 11 is not required to additionally provide for an altered ranking as recited in the claim(s).]
As per claim 13, Du in view of Yahalom teaches claim 10 as shown above and further teaches:
13. The computer-implemented method of claim 10, further comprising: evaluating, a combination of storage capacity and performance capacity caused by functioning of the plurality of volumes; and ranking a priority of modifying the storage system based on an aggregation of the storage capacity and performance capacity, wherein the priority is based on a quantity of the plurality of volumes employed. [Du in view of Yahalom as shown above teaches predicting resource loads reflecting usage of resources including computing and storage resources as well as providing rankings to the cluster nodes based on resource loads (Du: para. 7-8, 14, 20-21, 56-59, 73-80, 104-109; Yahalom: para. 45, 71-80; figs. 3A-C, 4, and associated paragraphs)]
Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 14, Du in view of Yahalom teaches claim 10 as shown above and further teaches:
14. The computer-implemented method of claim 10, further comprising: determining a utilization range for storage capacity or performance capacity for a volume in the plurality of volumes based on historical data; and triggering the forecasting upon determining that the volume is operating outside of the optimal utilization range. [Du teaches performing prediction of performance data for the preset time period responsive to determining that historical resource load of the cluster system is imbalanced, the imbalance determined based on the fifth standard deviation of the historical resource load (utilization range) being greater than a threshold (optimal utilization range) (para. 62-70; see para. 69-70 indicating historical performance data comprising computing/storage resource usage being used to calculate the historical resource load)]
As per claim 16,
16. A computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to execute operations, the operations comprising: [Du teaches cluster system comprising cluster nodes and a processor executing application program stored in memory (para. 3, 176-178)] forecasting performance metrics for a plurality of storage unit subsets of a storage system based on performance data for the storage system, wherein the performance metrics indicate a saturation of a capacity related to the plurality of storage unit subsets; [Du teaches predicting resource load (performance metric) of cluster nodes, comprising applications, for a preset time period based on performance data, the resource load and performance data reflecting usage of resources including computing and storage resources (para. 7-8, 14, 20-21, 48, 56-59, 73-80, 104-109); Du teaches distinguishing a cluster node with the heaviest resource load from the rest of the cluster nodes, wherein a migration of an application from the heaviest cluster node to another cluster node is simulated (and may be performed) for bringing resource load of the heaviest cluster node closer to an average resource load of the system (para. 114-124; 27-28, 48, 73-84, 104-108, 111, 113, 115; fig. 2, 2A and associated paragraphs)]
Du does not explicitly disclose, but Yahalom discloses:
ranking a plurality of storage units within the plurality of storage unit subsets based on the performance metrics to determine a ranking order of the plurality of the storage units to modify the storage system; and modifying the storage system in order of the ranking. [Du as shown above teaches simulating migration of an application from a cluster node with the heaviest resource load to another cluster node for bringing the resource load of the heaviest cluster node closer to an average resource node (see Du above); Yahalom discloses migration of a virtual machine from a storage component having a high storage utilization ranking to another storage component having a low storage utilization ranking for reducing storage utilization of the storage component having the high storage utilization ranking, wherein Yahalom uses respective thresholds for determining storage utilization rankings of storage components and for determining I/O traffic load generation rankings of the virtual machines (e.g. high or low ranking), and where Yahalom identifies a high ranking virtual machine in a high ranking storage component for migration to a low ranking storage component (para. 44-45, 71-80; figs. 3A-C, 4, and associated paragraphs); it would have been obvious for one of ordinary skill in the arts, provided with the disclosures by Du and Yahalom, both directed towards reducing load of a heavily utilized node, to provide for a combination comprising ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on high ranking cluster node to a low ranking cluster node. Doing so would allow for improved efficiency in determining migration destinations as well as applications to be subject to migration.
Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 17, Du in view of Yahalom teaches claim 16 as shown above and further teaches:
17. The computer program product of claim 16, wherein the operations further comprise: comparing a performance metric for a first volume to a performance metric for a second volume, wherein comparing the performance metric for the first volume to the performance metric for the second volume comprises: determining a synthetic indicator that comprises historical factors that are historically indicative of a capacity of the first volume or the second volume; and providing, the ranking relative to the first volume based on the comparing. [Du in view of Yahalom as shown above teaches ranking cluster nodes as high ranking or low ranking cluster nodes based on their resource loads at a predetermined time period using a threshold and migrating an application from a high ranking cluster node to a low ranking cluster node (see claim 1 above; Du: para. 7-8, 14, 20-21, 56-59, 73-80, 104-109, 124; Yahalom: para. 45, 71-80; figs. 3A-C, 4, and associated paragraphs; see fig. 4 and para. 79-80 of Yahalom providing the ranking process involving an initial ranking of storage components (comparing) (step 414) followed by the use of the threshold for determining high/low ranking storage components (steps 416, 418, 420))); Du teaches that the resource loads are predicted using historical performance data (Du: para. 73-80, 104-108)]
Du and Yahalom are analogous to the claimed invention because they are in the same field of endeavor involving data storage and workload balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du and Yahalom, to modify the disclosures by Du to include disclosures by Yahalom since they both teach data storage and workload balancing, wherein Yahalom is directed towards improved load balancing (para. 9). Therefore, it would be applying a known technique (providing rankings for virtual machines and storage components based on I/O traffic load and storage utilization, migrating a virtual machine with high I/O traffic load ranking from a storage component with high storage utilization ranking to a storage component with low storage utilization ranking for reducing storage utilization of the high ranking storage component) to a known device (migrating an application from a cluster node with the heaviest resource load to another cluster node for reducing resource load of the heaviest cluster node) ready for improvement to yield predictable results (ranking the applications and the cluster nodes based on resource load and prioritizing migration of high ranking application on a high ranking cluster node to a low ranking cluster node for improved efficiency in determining migration destinations as well as applications to be subject to migration). MPEP 2143
As per claim 20, Du in view of Yahalom teaches claim 16 as shown above and further teaches:
20. The computer program product of claim 16, wherein the operations further comprise: determining an optimal utilization range for storage capacity or performance capacity for the volume; and triggering the forecasting in a case where the plurality of volumes are operating outside of the optimal utilization range. [Du teaches performing prediction of performance data for the preset time period responsive to determining that historical resource load of the cluster system is imbalanced, the imbalance determined based on the fifth standard deviation of the historical resource load being greater than a threshold (optimal utilization range) (para. 62-70; see para. 69-70 indicating historical performance data comprising computing/storage resource usage being used to calculate the historical resource load)]
Claims 6-7, 15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Du et al. (US 20190253490 A1) in view of Yahalom et al. (US 20090172666 A1) in view of Chou et al. (US 20210176174 A1).
As per claim 6, Du in view of Yahalom teaches claim 1 as shown above. It does not explicitly disclose, but Chou teaches:
6. The system of claim 1, wherein the one or more processors further execute the one or more instructions to modify the storage system until a measured level of saturation of capacity at the storage system corresponding to the plurality of storage unit subsets satisfies a threshold value. [Du in view of Yahalom teaches performing a migration to reduce resource load of the cluster nodes below a preset threshold (Du: see para. 124 providing for comparison of a second standard deviation against a preset threshold); Chou teaches moving data between devices based on computing time information determined from performance information including computing capability, data storage amount, and maximum stored capacity, and further teaches recalculating the computing time and, responsive to the result not satisfying an evaluation condition, repeating the process (para. 11-12, 49)]
Du, Yahalom, and Chou are analogous to the claimed invention because they are in the same field of endeavor involving data storage and load balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du in view of Yahalom and Chou, to modify the disclosures by Du in view of Yahalom to include disclosures by Chou since they both teach data storage and load balancing, wherein Chou is directed towards improved load balancing in distributed network (para. 6-8). Therefore, it would be applying a known technique (determining whether an evaluation condition has been satisfied following movement of workload between devices; repeating the process responsive to the evaluation condition not having been satisfied) to a known device (system moving application from a cluster node to another cluster node to reduce resource load metric below a preset threshold) ready for improvement to yield predictable results (system moving application from a node to another node to reduce resource load metric below a preset threshold, re-calculating the resource load metric, and repeating the process if the preset threshold is not satisfied; doing so would provide for a more comprehensive method of achieving load balancing in spite of prediction/calculation errors). MPEP 2143
As per claim 7, Du in view of Yahalom teaches claim 1 as shown above. It does not explicitly disclose, but Chou teaches:
7. The system of claim 1, wherein the one or more processors further execute the one or more instructions to evaluate current performance data for the storage system based on the modification and determine whether a measured level of saturation of capacity at the storage system satisfies a threshold value. [Du in view of Yahalom teaches performing a migration to reduce resource load of the cluster nodes below a preset threshold (Du: see para. 124 providing for comparison of a second standard deviation against a preset threshold); Chou teaches moving data between devices based on computing time information determined from performance information including computing capability, data storage amount, and maximum stored capacity, and further teaches recalculating the computing time and, responsive to the result not satisfying an evaluation condition, repeating the process (para. 11-12, 49)]
Du, Yahalom, and Chou are analogous to the claimed invention because they are in the same field of endeavor involving data storage and load balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du in view of Yahalom and Chou, to modify the disclosures by Du in view of Yahalom to include disclosures by Chou since they both teach data storage and load balancing, wherein Chou is directed towards improved load balancing in distributed network (para. 6-8). Therefore, it would be applying a known technique (determining whether an evaluation condition has been satisfied following movement of workload between devices; repeating the process responsive to the evaluation condition not having been satisfied) to a known device (system moving application from a cluster node to another cluster node to reduce resource load metric below a preset threshold) ready for improvement to yield predictable results (system moving application from a node to another node to reduce resource load metric below a preset threshold, re-calculating the resource load metric, and repeating the process if the preset threshold is not satisfied; doing so would provide for a more comprehensive method of achieving load balancing in spite of prediction/calculation errors). MPEP 2143
As per claim 15, Du in view of Yahalom teaches claim 10 as shown above. It does not explicitly disclose, but Chou teaches:
15. The computer-implemented method of claim 10, further comprising modifying the storage system until a measured level of saturation of capacity at the storage system corresponding to the plurality of volumes satisfies a threshold value for non-saturated functioning. [Du in view of Yahalom teaches performing a migration to reduce resource load of the cluster nodes below a preset threshold (Du: see para. 124 providing for comparison of a second standard deviation against a preset threshold); Chou teaches moving data between devices based on computing time information determined from performance information including computing capability, data storage amount, and maximum stored capacity, and further teaches recalculating the computing time and, responsive to the result not satisfying an evaluation condition, repeating the process (para. 11-12, 49)]
Du, Yahalom, and Chou are analogous to the claimed invention because they are in the same field of endeavor involving data storage and load balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du in view of Yahalom and Chou, to modify the disclosures by Du in view of Yahalom to include disclosures by Chou since they both teach data storage and load balancing, wherein Chou is directed towards improved load balancing in distributed network (para. 6-8). Therefore, it would be applying a known technique (determining whether an evaluation condition has been satisfied following movement of workload between devices; repeating the process responsive to the evaluation condition not having been satisfied) to a known device (system moving application from a cluster node to another cluster node to reduce resource load metric below a preset threshold) ready for improvement to yield predictable results (system moving application from a node to another node to reduce resource load metric below a preset threshold, re-calculating the resource load metric, and repeating the process if the preset threshold is not satisfied; doing so would provide for a more comprehensive method of achieving load balancing in spite of prediction/calculation errors). MPEP 2143
As per claim 18, Du in view of Yahalom teaches claim 16 as shown above. It does not explicitly disclose, but Chou teaches:
18. The computer program product of claim 16, wherein the operations further comprise: re-evaluating based on the modification at the storage system, performance data for the storage system; and determining whether a measured level of saturation of capacity at the storage system satisfies the selected threshold value. [Du in view of Yahalom teaches performing a migration to reduce resource load of the cluster nodes below a preset threshold (Du: see para. 124 providing for comparison of a second standard deviation against a preset threshold); Chou teaches moving data between devices based on computing time information determined from performance information including computing capability, data storage amount, and maximum stored capacity, and further teaches recalculating the computing time and, responsive to the result not satisfying an evaluation condition, repeating the process (para. 11-12, 49)]
Du, Yahalom, and Chou are analogous to the claimed invention because they are in the same field of endeavor involving data storage and load balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du in view of Yahalom and Chou, to modify the disclosures by Du in view of Yahalom to include disclosures by Chou since they both teach data storage and load balancing, wherein Chou is directed towards improved load balancing in distributed network (para. 6-8). Therefore, it would be applying a known technique (determining whether an evaluation condition has been satisfied following movement of workload between devices; repeating the process responsive to the evaluation condition not having been satisfied) to a known device (system moving application from a cluster node to another cluster node to reduce resource load metric below a preset threshold) ready for improvement to yield predictable results (system moving application from a node to another node to reduce resource load metric below a preset threshold, re-calculating the resource load metric, and repeating the process if the preset threshold is not satisfied; doing so would provide for a more comprehensive method of achieving load balancing in spite of prediction/calculation errors). MPEP 2143
As per claim 19, Du in view of Yahalom teaches claim 16 as shown above. It does not explicitly disclose, but Chou teaches:
19. The computer program product of claim 16, wherein the operations further comprise modifying the storage system until a measured level of saturation of capacity corresponding to the plurality of volumes satisfies the threshold value. [Du in view of Yahalom teaches performing a migration to reduce resource load of the cluster nodes below a preset threshold (Du: see para. 124 providing for comparison of a second standard deviation against a preset threshold); Chou teaches moving data between devices based on computing time information determined from performance information including computing capability, data storage amount, and maximum stored capacity, and further teaches recalculating the computing time and, responsive to the result not satisfying an evaluation condition, repeating the process (para. 11-12, 49)]
Du, Yahalom, and Chou are analogous to the claimed invention because they are in the same field of endeavor involving data storage and load balancing.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, having knowledge of Du in view of Yahalom and Chou, to modify the disclosures by Du in view of Yahalom to include disclosures by Chou since they both teach data storage and load balancing, wherein Chou is directed towards improved load balancing in distributed network (para. 6-8). Therefore, it would be applying a known technique (determining whether an evaluation condition has been satisfied following movement of workload between devices; repeating the process responsive to the evaluation condition not having been satisfied) to a known device (system moving application from a cluster node to another cluster node to reduce resource load metric below a preset threshold) ready for improvement to yield predictable results (system moving application from a node to another node to reduce resource load metric below a preset threshold, re-calculating the resource load metric, and repeating the process if the preset threshold is not satisfied; doing so would provide for a more comprehensive method of achieving load balancing in spite of prediction/calculation errors). MPEP 2143
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Gururaj et al. (US 20200250002 A1) teaches predicting future condition of a node and assigning a score reflective of the ability of the node to execute an application workload.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIAS KIM whose telephone number is (571)272-8093. The examiner can normally be reached Monday - Friday: 7:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JARED RUTZ can be reached at 571-272-5535. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.Y.K./Examiner, Art Unit 2135 /JARED I RUTZ/Supervisory Patent Examiner, Art Unit 2135