DETAILED ACTION
Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-14 and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Black et al. (US 12,147,664 B2).
As to claim 1, Black teaches a system, comprising:
at least one processor (a processor; col. 30, line 22); and
a memory that stores executable instructions that, when executed by the at least
one processor, facilitate performance of operations (computer readable program instructions thereon for causing a processor to conduct aspects of the one or more embodiments described herein; col. 30, lines 20-23), the operations comprising:
obtaining input / output (I/O) operation data representative of I/O operations
corresponding to workload data representative of workloads maintained in a storage
system (the storage maintenance system 302 can proactively address capacity, performance and/or efficiency of the storage system 328. First, the determination component 312 can receive, transmit, locate, identify and/or otherwise obtain various data (e.g., including metadata) that can be employed by the evaluation component 316. The data can be obtained by evaluating applications 334 at the storage system 328. The data can be obtained from logs and/or monitoring. The data can comprise capacity data such as related to and/or defining capacity, performance, efficiency, throughput, rates order of operations performed, number of operations performed, number of times a same operation is performed, workload characterization, and/or changes in storage system data due to use of an application 334 and/or data object 333 employed by the application 334; col. 23, line 11-27);
classifying respective portions of the I/O operation data into respective workload
pattern datasets (The evaluation component 316 can, based on the data obtained by the determination component 312, determine aspects of performance data 410 (FIG. 4A) for an application 334. The performance data can be related to any of bandwidth, usage, utilization, memory, latency, power, rates and/or operations. The determination can comprise aggregating the data, searching the data, and/or identifying aspects of the data related to one or more defined performance data types and/or performance categories 420.; col. 13, lines 1-9);
processing the respective workload pattern datasets by respective trained models
to determine respective policy recommendation data representative of respective policy
recommendations with respect to storage system resources and storage system resource
usage (In one or more embodiments, the prediction component 318 can, based on the performance data 410 and/or performance category 420 for an application 334, predict a type of application and/or use of an application. A “type” can refer to application category such as word processing, spreadsheet, presentation, multimedia, web browser, graphics related, education, resource planning, customer relationship management, and/or the like. A “use” can refer to any definition related to a workload, such as repeated use, order of use, non-use and/or sequential use. This prediction can be employed to further label and/or characterize an application 334, again without compromising a state of non-access to data of, comprised by and/or making up the application 334. This prediction can be employed to determine a grouping 430 and/or to generate a change determination 440, among other uses, such as by the evaluation component 316.; col. 14, lines 41-56, col. 15, lines 38-54, and “In one or more embodiments, the storage maintenance system 302 can comprise an analytical model 322. The analytical model 322 can be, can comprise and/or can be comprised by a classical model, such as a predictive model, neural network, and/or artificial intelligent model. An artificial intelligent model and/or neural network (e.g., a convolutional network and/or deep neural network) can comprise and/or employ artificial intelligence (AI), machine learning (ML), and/or deep learning (DL), where the learning can be supervised, semi-supervised, self-supervised, semi-self-supervised and/or unsupervised. For example, the analytical model 322 can comprise an ML model.”; col. 17, lines 31-42 and “the analytical model 322 can aid the evaluation component 316 in generating such one or more predictions of type and/or use of one or more applications 334. That is, the analytical model 322 can obtain the performance data 410 and/or any other relevant workload characterization data, compare such data to historical performance data and/or historical workload characterizations, and based on the comparison, generate the one or more predictions”; col. 18, lines 3-11); and
outputting the respective policy recommendation data (“The execution component 320 can execute such modification and/or modifications one at a time and/or with any two or more modification being executed at least partially in parallel with one another”; col. 16, lines 40-43, and “A first example modification that can be executed (e.g., by the execution component 320) at the storage system 600 can be generation of and implementation of a common policy 650 that affects both applications 612A and 612D. For example, each of these objects can have high usage relative to a workload or a high prioritized client entity. The common policy 650 can comprise increased access priority, common I/O control rate and/or common I/O throughput to applications 612A and 612D. Such modification can increase storage system efficiency and storage system performance”; col. 19, lines 8-12 and lines 22-31).
As to claim 2, Black teaches the system of claim 1, wherein the classifying of the respective portions comprises classifying one workload pattern dataset of the respective workload pattern datasets as: random write data representative of at least one random write, random read data representative of at least one random read, sequential write data representative of at least one sequential write, or sequential read data representative of at least one sequential read (a performance category with the data object 234. In accordance with various embodiments, examples of performance data can comprise but are not limited to, input/output (I/O) rate, I/O throughput, I/O size, random versus sequential usage, read/write ratio, and/or storage system location; col. 10, lines 17-20).
As to claim 3, Black teaches the system of claim 1, wherein the classifying of the respective portions comprises classifying one workload pattern dataset of the respective workload pattern datasets as: transaction-related data related to at least one transaction, virtualization-related data related to at least one virtualization, or big data of at least a defined size (a performance category with the data object 234. In accordance with various embodiments, examples of performance data can comprise but are not limited to, input/output (I/O) rate, I/O throughput, I/O size, random versus sequential usage, read/write ratio, and/or storage system location; col. 10, lines 17-20).
As to claim 4, Black teaches the system of claim 1, wherein determining the respective policy recommendation data comprises predicting, by the respective trained models, respective storage region data representative of respective storage regions, and wherein the respective policy recommendation data is determined based on the respective storage region data (“a performance category with the data object 234. In accordance with various embodiments, examples of performance data can comprise but are not limited to, input/output (I/O) rate, I/O throughput, I/O size, random versus sequential usage, read/write ratio, and/or storage system location”; col. 10, lines 17-20 and “the analytical model 322 can be trained on new software and/or hardware of the storage system 328, such as relative to a new node, aggregate, volume, disk and/or application 334 at the storage system 328”; col. 28, lines 31-34).
As to claim 5, Black teaches the system of claim 4, wherein the predicting of the respective storage region data comprises predicting at least one of: hot storage region data representative of at least one hot storage region, cold storage region data representative of at least one cold storage region, future write storage region data representative of at least one future write storage region, future read storage region data representative of at least one future read storage region, or locality storage region data representative of at least one locality storage region (The modification at the storage system can comprise changing a functioning of the storage system comprising that a different aggregate and/or node can be used to access an application that has been moved, that a read/write/get can refer to a different aggregate and/or node due to the application move, that similar applications and/or related data objects can be stored together, that one or more applications and/or related data objects can be de-duplicated, and/or that one or more workloads, nodes, aggregates, volumes and/or the storage system can operate with increased efficiency, capacity and/or performance; col. 16, lines 29-39).
As to claim 6, Black teaches the system of claim 1, wherein determining the respective policy recommendation data comprises determining at least one of: storage-related tier data representative of at least one storage-related tier, storage-related mirroring data related to storage
mirroring, storage-related erasure coding data related to storage erasure coding, storage-related
deduplication data related to storage deduplication, storage-related compression data related to
storage compression, storage cache-related data related to at least one storage cache, storage-
related read ahead region data related to at least one storage read ahead region, storage-related
deduplication data related to storage deduplication, or processing usage data related to usage of
at least one processing unit (“A suggested change can be for any application 334 and/or data object 333 that would address a storage system capacity, performance and/or efficiency. A suggested modification at the storage system can comprise but is not limited to generation of a policy 450 (e.g., directed to access, priority, order of operations, data protection, data retention and/or data de-duplication) and/or arrangement 452 (e.g., movement, copy, de-duplication) of an application 334 and/or data object 333”; col. 15, lines 46-54 and “The modification at the storage system can comprise changing a functioning of the storage system comprising that a different aggregate and/or node can be used to access an application that has been moved, that a read/write/get can refer to a different aggregate and/or node due to the application move, that similar applications and/or related data objects can be stored together, that one or more applications and/or related data objects can be de-duplicated, and/or that one or more workloads, nodes, aggregates, volumes and/or the storage system can operate with increased efficiency, capacity and/or performance.”; col. 16, lines 29-39).
As to claim 7, Black teaches the system of claim 1, wherein determining the respective policy recommendation data comprises determining at least one of: recommended processor resources, recommended memory resources, recommended storage device resources, recommended virtual machines, or recommended docker containers (generate one or more suggested modifications to be implemented at the storage system 328, which one or more suggested modifications can be provided in the form of a change determination 440 (FIG. 4A). A suggested change can be for any application 334 and/or data object 333 that would address a storage system capacity, performance and/or efficiency. A suggested modification at the storage system can comprise but is not limited to generation of a policy 450 (e.g., directed to access, priority, order of operations, data protection, data retention and/or data de-duplication) and/or arrangement 452 (e.g., movement, copy, de-duplication) of an application 334 and/or data object 333.; col. 15, lines 43-54).
As to claim 8, Black teaches the system of claim 1, wherein the respective trained models comprise at least one of: at least one clustering model, or at least one time series analysis model (continually training analytical models employed by the system, at a suitable frequency. The analytical models can be employed generate the correlations, groupings and/or change determinations to be implemented at the storage system to achieve the aforementioned storage system increase in performance and/or efficiency. Due to the updating, subsequent iterations of use of the one or more of the aforementioned system, computer program product and/or computer-implemented method can be made more accurate and/or efficient; col. 2, line 61 – col. 3, line 3).
As to claim 9, Black teaches the system of claim 1, wherein the obtaining of the 1/0 operation metadata comprises obtaining at least one of: workload intensity data representative of at least one intensity associated with at least one workload, I/O latency data representative of at least one latency associated with at least one I/O operation of the I/O operations, processing unit usage data related to usage of at least one processing unit, or memory usage data related to usage of at least one storage unit (The evaluation component 316 can, based on the data obtained by the determination component 312, determine aspects of performance data 410 (FIG. 4A) for an application 334. The performance data can be related to any of bandwidth, usage, utilization, memory, latency, power, rates and/or operations; col. 13, lines 1-6).
As to claim 10, Black teaches the system of claim 1, wherein the obtaining of the I/O operation data comprises obtaining, for respective I/O operations, at least one of: respective filename data representative of respective filenames, respective volume data representative of respective volumes, respective timestamp data representative of respective timestamps, respective I/O command data representative of respective I/O commands, respective offset data representative of respective offsets, respective logical block addressing data representative of respective logical block addresses, respective length data representative of respective lengths of the I/O operations, respective pattern data representative of respective patterns associated with the I/O operations, or respective I/O latency data representative of respective I/O latencies of the I/O operations (A data object can refer to a volume, file, data block LUN and/or S3 object; col. 6, lines 65-67).
As to claim 11, Black teaches the system of claim 1, wherein the obtaining of the 1/O operation data comprises using, for any of the I/O operation data exchanged between a server and a storage volume, at least one of: a dynamic tracing tool or a packet analyzer tool (First, the determination component 312 can receive, transmit, locate, identify and/or otherwise obtain various data (e.g., including metadata) that can be employed by the evaluation component 316. The data can be obtained by evaluating applications 334 at the storage system 328. The data can be obtained from logs and/or monitoring. The data can comprise capacity data such as related to and/or defining capacity, performance, efficiency, throughput, rates order of operations performed, number of operations performed, number of times a same operation is performed, workload characterization, and/or changes in storage system data due to use of an application 334 and/or data object 333 employed by the application 334.; col. 12, lines 15-27).
As to claim 12, Black teaches a method (computer-implemented method; col. 1, line33), comprising:
obtaining, by a system comprising at least one processor, collected input / output (I/O)
operation data corresponding to workload data maintained in a storage system (the storage maintenance system 302 can proactively address capacity, performance and/or efficiency of the storage system 328. First, the determination component 312 can receive, transmit, locate, identify and/or otherwise obtain various data (e.g., including metadata) that can be employed by the evaluation component 316. The data can be obtained by evaluating applications 334 at the storage system 328. The data can be obtained from logs and/or monitoring; col. 23, line 11-27);
extracting, by the system from the collected I/O operation data, feature data (The data can comprise capacity data such as related to and/or defining capacity, performance, efficiency, throughput, rates order of operations performed, number of operations performed, number of times a same operation is performed, workload characterization, and/or changes in storage system data due to use of an application 334 and/or data object 333 employed by the application 334; col. 23, line 11-27);
inputting, by the system, the feature data into trained models to obtain at least one of:
storage region data, or resource usage data (the analytical model 322 can be trained, such as by a training component 324, on a set of training data that can represent the type of data for which the storage maintenance system 302 will be used. That is, the analytical model 322 can be trained on historical and/or current data comprising performance data such as defining/representing workload characterization and/or application performance. Likewise, the analytical model 322 can be trained on new software and/or hardware of the storage system 328, such as relative to a new node, aggregate, volume, disk and/or application 334 at the storage system 328; col. 18, lines 24-34); and
outputting recommendation data comprising policy data based on at least one of: the
storage region data, or the resource usage data (In one or more embodiments, the prediction component 318 can, based on the performance data 410 and/or performance category 420 for an application 334, predict a type of application and/or use of an application. A “type” can refer to application category such as word processing, spreadsheet, presentation, multimedia, web browser, graphics related, education, resource planning, customer relationship management, and/or the like. A “use” can refer to any definition related to a workload, such as repeated use, order of use, non-use and/or sequential use. This prediction can be employed to further label and/or characterize an application 334, again without compromising a state of non-access to data of, comprised by and/or making up the application 334. This prediction can be employed to determine a grouping 430 and/or to generate a change determination 440, among other uses, such as by the evaluation component 316.; col. 14, lines 41-56, col. 15, lines 38-54, and “In one or more embodiments, the storage maintenance system 302 can comprise an analytical model 322. The analytical model 322 can be, can comprise and/or can be comprised by a classical model, such as a predictive model, neural network, and/or artificial intelligent model. An artificial intelligent model and/or neural network (e.g., a convolutional network and/or deep neural network) can comprise and/or employ artificial intelligence (AI), machine learning (ML), and/or deep learning (DL), where the learning can be supervised, semi-supervised, self-supervised, semi-self-supervised and/or unsupervised. For example, the analytical model 322 can comprise an ML model.”; col. 17, lines 31-42 and “the analytical model 322 can aid the evaluation component 316 in generating such one or more predictions of type and/or use of one or more applications 334. That is, the analytical model 322 can obtain the performance data 410 and/or any other relevant workload characterization data, compare such data to historical performance data and/or historical workload characterizations, and based on the comparison, generate the one or more predictions”; col. 18, lines 3-11).
As to claim 13, Black teaches the method of claim 12, wherein the inputting of the feature data into the trained models comprises inputting the feature data into at least one of: a time series model, a neural network mode, a clustering model, or a regression model (“continually training analytical models employed by the system, at a suitable frequency. The analytical models can be employed generate the correlations, groupings and/or change determinations to be implemented at the storage system to achieve the aforementioned storage system increase in performance and/or efficiency. Due to the updating, subsequent iterations of use of the one or more of the aforementioned system, computer program product and/or computer-implemented method can be made more accurate and/or efficient”; col. 2, line 61 – col. 3, line 3 and “In one or more embodiments, the storage maintenance system 302 can comprise an analytical model 322. The analytical model 322 can be, can comprise and/or can be comprised by a classical model, such as a predictive model, neural network, and/or artificial intelligent model. An artificial intelligent model and/or neural network (e.g., a convolutional network and/or deep neural network) can comprise and/or employ artificial intelligence (AI), machine learning (ML), and/or deep learning (DL), where the learning can be supervised, semi-supervised, self-supervised, semi-self-supervised and/or unsupervised. For example, the analytical model 322 can comprise an ML model.”; col. 17, lines 31-42).
As to claim 14, Black teaches the method of claim 12, wherein the outputting of the recommendation data comprises outputting policy data for at least one of: storage-related tier data, storage-related mirroring data, storage-related erasure coding data, storage-related deduplication data, storage-related compression data, storage cache-related data, storage-related read ahead region data, storage-related deduplication data, processor data, memory data, storage device data, virtual machine data, or docker container data (The modification at the storage system can comprise changing a functioning of the storage system comprising that a different aggregate and/or node can be used to access an application that has been moved, that a read/write/get can refer to a different aggregate and/or node due to the application move, that similar applications and/or related data objects can be stored together, that one or more applications and/or related data objects can be de-duplicated, and/or that one or more workloads, nodes, aggregates, volumes and/or the storage system can operate with increased efficiency, capacity and/or performance; col. 16, lines 29-39).
As to claim 17, Black teaches the method of claim 16, wherein the inputting of the feature data into the trained models comprises inputting the feature data into at least one of: a clustering model, a regression model, or a heat map model (continually training analytical models employed by the system, at a suitable frequency. The analytical models can be employed generate the correlations, groupings and/or change determinations to be implemented at the storage system to achieve the aforementioned storage system increase in performance and/or efficiency. Due to the updating, subsequent iterations of use of the one or more of the aforementioned system, computer program product and/or computer-implemented method can be made more accurate and/or efficient; col. 2, line 61 – col. 3, line 3).
As to claim 18, Black teaches a non-transitory machine-readable medium (non-transitory computer-readable medium; col. 2, lines 9-10), comprising executable instructions that, when executed by at least one processor, facilitate performance of operations (computer readable program instructions thereon for causing a processor to conduct aspects of the one or more embodiments described herein; col. 30, lines 20-23), the operations comprising:
obtaining input / output (I/O) operation data corresponding to workload data maintained
in a storage system (the storage maintenance system 302 can proactively address capacity, performance and/or efficiency of the storage system 328. First, the determination component 312 can receive, transmit, locate, identify and/or otherwise obtain various data (e.g., including metadata) that can be employed by the evaluation component 316. The data can be obtained by evaluating applications 334 at the storage system 328. The data can be obtained from logs and/or monitoring. The data can comprise capacity data such as related to and/or defining capacity, performance, efficiency, throughput, rates order of operations performed, number of operations performed, number of times a same operation is performed, workload characterization, and/or changes in storage system data due to use of an application 334 and/or data object 333 employed by the application 334; col. 23, line 11-27);
obtaining system monitoring data corresponding to the I/O operation data (In accordance with various embodiments, examples of performance data can comprise but are not limited to, input/output (I/O) rate, I/O throughput, I/O size, random versus sequential usage, read/write ratio, and/or storage system location; col. 10, lines 17-20);
classifying respective portions of the I/O operation data into respective I/O pattern
datasets ((The evaluation component 316 can, based on the data obtained by the determination component 312, determine aspects of performance data 410 (FIG. 4A) for an application 334. The performance data can be related to any of bandwidth, usage, utilization, memory, latency, power, rates and/or operations. The determination can comprise aggregating the data, searching the data, and/or identifying aspects of the data related to one or more defined performance data types and/or performance categories 420.; col. 13, lines 1-9));
determining, from the system monitoring data, workload intensity data, I/O latency data,
processing units usage data and memory usage data (The evaluation component 316 can, based on the data obtained by the determination component 312, determine aspects of performance data 410 (FIG. 4A) for an application 334. The performance data can be related to any of bandwidth, usage, utilization, memory, latency, power, rates and/or operations; col. 13, lines 1-9 and “As used herein, “performance” can refer to lack of latency, resource usage and/or resource utilization. As used herein, “capacity” can refer to storage capacity, remaining capacity, efficient use of capacity and/or capacity savings. As used herein, “efficiency” can refer to decreased latency, increased resource usage and/or increased resource utilization.”; col. 2, lines 46-52);
processing the respective I/O pattern datasets by respective first trained models to
determine respective first respective policy recommendation data with respect to storage system
resources and storage system resource usage (In one or more embodiments, the prediction component 318 can, based on the performance data 410 and/or performance category 420 for an application 334, predict a type of application and/or use of an application. A “type” can refer to application category such as word processing, spreadsheet, presentation, multimedia, web browser, graphics related, education, resource planning, customer relationship management, and/or the like. A “use” can refer to any definition related to a workload, such as repeated use, order of use, non-use and/or sequential use. This prediction can be employed to further label and/or characterize an application 334, again without compromising a state of non-access to data of, comprised by and/or making up the application 334. This prediction can be employed to determine a grouping 430 and/or to generate a change determination 440, among other uses, such as by the evaluation component 316.; col. 14, lines 41-56, col. 15, lines 38-54, and “In one or more embodiments, the storage maintenance system 302 can comprise an analytical model 322. The analytical model 322 can be, can comprise and/or can be comprised by a classical model, such as a predictive model, neural network, and/or artificial intelligent model. An artificial intelligent model and/or neural network (e.g., a convolutional network and/or deep neural network) can comprise and/or employ artificial intelligence (AI), machine learning (ML), and/or deep learning (DL), where the learning can be supervised, semi-supervised, self-supervised, semi-self-supervised and/or unsupervised. For example, the analytical model 322 can comprise an ML model.”; col. 17, lines 31-42 and “the analytical model 322 can aid the evaluation component 316 in generating such one or more predictions of type and/or use of one or more applications 334. That is, the analytical model 322 can obtain the performance data 410 and/or any other relevant workload characterization data, compare such data to historical performance data and/or historical workload characterizations, and based on the comparison, generate the one or more predictions”; col. 18, lines 3-11);
processing the workload intensity data, I/O latency data, processing unit usage data and
memory usage data by second trained models to determine second policy recommendation data
with respect to recommending at least one of: changing memory size, changing a number of
processing units, or changing storage devices (The modification at the storage system can comprise changing a functioning of the storage system comprising that a different aggregate and/or node can be used to access an application that has been moved, that a read/write/get can refer to a different aggregate and/or node due to the application move, that similar applications and/or related data objects can be stored together, that one or more applications and/or related data objects can be de-duplicated, and/or that one or more workloads, nodes, aggregates, volumes and/or the storage system can operate with increased efficiency, capacity and/or performance; col. 16, lines 29-39); and
outputting the first policy recommendation data and the second policy recommendation
data (“The execution component 320 can execute such modification and/or modifications one at a time and/or with any two or more modification being executed at least partially in parallel with one another”; col. 16, lines 40-43, and “A first example modification that can be executed (e.g., by the execution component 320) at the storage system 600 can be generation of and implementation of a common policy 650 that affects both applications 612A and 612D. For example, each of these objects can have high usage relative to a workload or a high prioritized client entity. The common policy 650 can comprise increased access priority, common I/O control rate and/or common I/O throughput to applications 612A and 612D. Such modification can increase storage system efficiency and storage system performance”; col. 19, lines 8-12 and lines 22-31).
As to claim 19, Black teaches the non-transitory machine-readable medium of claim 18, wherein the processing of the respective I/O pattern datasets comprises inputting the respective I/O pattern datasets into at least one of: clustering models, regression models, or time series models (The analytical models can be employed generate the correlations, groupings and/or change determinations to be implemented at the storage system to achieve the aforementioned storage system increase in performance and/or efficiency. Due to the updating, subsequent iterations of use of the one or more of the aforementioned system, computer program product and/or computer-implemented method can be made more accurate and/or efficient; col. 2, line 61 – col. 3, line 3 and “In one or more embodiments, the storage maintenance system 302 can comprise an analytical model 322. The analytical model 322 can be, can comprise and/or can be comprised by a classical model, such as a predictive model, neural network, and/or artificial intelligent model. An artificial intelligent model and/or neural network (e.g., a convolutional network and/or deep neural network) can comprise and/or employ artificial intelligence (AI), machine learning (ML), and/or deep learning (DL), where the learning can be supervised, semi-supervised, self-supervised, semi-self-supervised and/or unsupervised. For example, the analytical model 322 can comprise an ML model.”; col. 17, lines 31-42) to determine at least one of: storage region data, locality region data, or forecast region data, and wherein the first respective policy recommendation data is based on the at least one of the: storage region data, locality region data or forecast region data (“a performance category with the data object 234. In accordance with various embodiments, examples of performance data can comprise but are not limited to, input/output (I/O) rate, I/O throughput, I/O size, random versus sequential usage, read/write ratio, and/or storage system location”; col. 10, lines 17-20.
As to claim 20, Black teaches the non-transitory machine-readable medium of claim 18, wherein the classifying of the respective portions comprises classifying the respective I/O pattern datasets into at least one of: random write data, random read data, sequential write data, sequential read data, transactional data, virtualization-related data, or big data (a performance category with the data object 234. In accordance with various embodiments, examples of performance data can comprise but are not limited to, input/output (I/O) rate, I/O throughput, I/O size, random versus sequential usage, read/write ratio, and/or storage system location; col. 10, lines 17-20).
Allowable Subject Matter
Claims 15 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
As to claim 15, the prior art of record does not teach or render obvious the limitations recited in claim 15, when taken in the context of the claims as a whole, specific to wherein the feature data comprises sequential write data and sequential read write data, wherein the storage region data comprises forecasted future write region data based on the sequential write data, and forecasted future read region data based on the sequential read data, wherein the inputting of the feature data into the trained models comprises inputting the feature data into time series models, and wherein the outputting of the recommendation data comprises outputting first policy data corresponding to cache usage data and using parity-based erasure coding for the forecasted future write region data, and outputting second policy data corresponding to cache usage data and read ahead data region data for the forecasted future read region data.
As to claim 16, the prior art of record does not teach or render obvious the limitations recited in claim 16, when taken in the context of the claims as a whole, specific to wherein the feature data comprises random write data and random read write data, wherein the storage region data comprises hot region data and cold region data based on the random write data, and read locality region data based on the random read data, and wherein the outputting of the recommendation data comprises outputting first policy data corresponding to first tier usage data and using data mirroring for the hot region data, and outputting second policy data corresponding to second tier usage data and avoiding using compression for the read locality region data.
Moreover, evidence for modifying the prior art teachings by one of ordinary skill level in the art was not uncovered so as to result in the invention as recited in claims 15 and 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kumar et al. (US 10,412,155 B2) teaches systems and methods are disclosed for managing workload among server clusters is disclosed. According to certain embodiments, the system may include a memory storing instructions and a processor. The processor may be configured to execute the instructions to determine historical behaviors of the server clusters in processing a workload. The processor may also be configured to execute the instructions to construct cost models for the server clusters based at least in part on the historical behaviors. The cost model is configured to predict a processor utilization demand of a workload. The processor may further be configured to execute the instructions to receive a workload and determine efficiencies of processing the workload by the server clusters based at least in part on at least one of the cost models or an execution plan of the workload.
Dar et al. (US 2023/0342280 A1) teaches method, computer program product, and computing system for processing historical input/output (IO) performance data associated with one or more storage objects of a storage system. A plurality of IO modeling systems may be trained using the historical IO performance data. Modeling performance information may be determined for the plurality of IO modeling systems across the historical IO performance data. A forecast score may be determined for each IO modeling system based on the modeling performance information for the plurality of IO modeling systems. A subset of the plurality of IO modeling systems may be selected based upon the forecast score for each IO modeling system. The at least one IO modeling system may be trained using the historical IO performance data. IO performance data may be forecasted using the at least one trained IO modeling system from the subset of the plurality of IO modeling systems.
Naamad et al. (US 11,853,656 B1) teaches techniques for modeling processing performed in a data storage system. Inputs received may include a plurality of workloads each denoting a workload for one of a plurality of storage groups, a plurality of service level objectives each denoting a target level of performance for one of the plurality of storage groups, a plurality of capacities each denoting a storage capacity of one of a plurality of storage tiers, and a plurality of maximum workloads each denoting a maximum workload capability of one of the plurality of storage tiers. Using the inputs, placement of data of the plurality of storage groups on the plurality of storage tiers may be modeled. Output(s) may be generated based on the modeling where the output(s) may include an amount of each of the plurality of storage tiers allocated by modeling to each of the plurality of storage groups.
Base et al. (US 12,536,040 B2) teaches a method for data sequence prediction and resource allocation includes determining, by a memory system, a plurality of resource parameters associated with operation of the memory system and determining respective time intervals associated with usage patterns corresponding to the memory system, the respective time intervals being associated with one or more sets of the plurality of resource parameters. The method further includes determining, using the plurality of resource parameters, one or more weights for hidden layers of a neural network for the respective time intervals associated with the usage patterns and allocating computing resources within the memory system for use in execution of workloads based on the determined one or more weights for hidden layers of the neural network.
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/DIEM K CAO/Primary Examiner, Art Unit 2196
DC
June 4, 2026
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