# US 11,029,972 B2 · Method and system for profile learning window optimization > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,029,972 B2 - Document ID: US11029972B2 - Status: Granted - Kind: B2 - Application number: 16/265,464 - Filed: 2019-02-01 - Published: 2020-08-06 - Granted: 2021-06-08 - Earliest claimed: 2019-02-01 - Assignees: Dell Products L.P. - Applicants: Dell Products, LP - Inventors: Nikhil M. Vichare; Farzad Khosrowpour - Web page: https://patentgrep.com/patent/US11029972B2 ## CPC - G06F 9/44505: {Configuring for program initiating, e.g. using registry, configuration files} - G06F 9/5083: {Techniques for rebalancing the load in a distributed system} - G06F 11/3024: {where the computing system component is a central processing unit [CPU]} - G06F 11/3089: {Monitoring arrangements determined by the means or processing involved in sensing the monitored data, e.g. interfaces, connectors, sensors, probes, agents (software debugging using additional hardware using a specific debug interface G06F11/3656; performance evaluation by tracing or monitoring G06F11/3466)} - G06F 11/3409: {for performance assessment} - G06F 11/3447: {Performance evaluation by modeling} - G06F 11/3466: {Performance evaluation by tracing or monitoring} - G06N 20/00: Machine learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract An information handling system operating a performance optimization system may comprise a processor executing computer program code instructions that interact with a plurality of computer operations and that is configured for iteratively sampling field performance data of the information handling system during learning windows having a preset duration and occurring at a preset frequency according to optimal learning window parameters, and adjusting the performance of the information handling system via adjustment of optimized system configurations based on application of a predetermined statistical model to the iteratively sampled field performance data. The optimal learning window parameters may be determined based on accuracy of previous application of the predetermined statistical model to test performance data of the information handling system. ## Claims ### Claim 1 An information handling system operating a performance optimization system comprising: a data bus coupled to a processor and a non-transitory, computer-readable storage medium; and the processor executing computer program code instructions that interact with a plurality of computer operations and that is configured for: inputting an application workload into a predetermined statistical model to output a predicted optimized hardware configuration for the information handling system; iteratively sampling hardware component field performance data describing performance of a hardware component of the information handling system during one or more learning windows, the learning windows having a preset duration and occurring at a preset frequency according to optimal learning window parameters to decrease computing resources consumed during determination of the predicted optimized hardware configuration by the predetermined statistical model, wherein the optimal learning window parameters are determined based on accuracy of previous application of the predetermined statistical model to previous hardware component test performance data as previously sampled during testing; adjusting performance of the hardware component of the information handling system by configuring the hardware component according to the predicted optimized hardware configuration based on application of the predetermined statistical model to the iteratively sampled field performance data. ### Claim 2 (depends on 1) The information handling system of claim 1, wherein the processor executing computer program code instructions is further configured for: characterizing workloads in operation at runtime to analyze performance of the hardware component of the information handling system during usage; and identifying a the hardware component configuration to be changed to obtain optimal performance of the hardware component based upon the characterizing of workloads. ### Claim 3 (depends on 2) The information handling system of claim 2, wherein the adjustment of the performance of the hardware component is performed dynamically for varying workloads to account for varying workloads, the varying workloads comprising random stochastic variation in workload, or abrupt user or operating system level discontinuities in jobs. ### Claim 4 (depends on 1) The information handling system of claim 1, wherein the optimal learning window parameters includes an optimal voting method and the optimized system configurations are determined based upon the optimal voting method. ### Claim 5 (depends on 1) The information handling system of claim 1, wherein the accuracy of previous application of the predetermined statistical model is defined by an area under a receiver operating characteristic curve for the previous application of the predetermined statistical model. ### Claim 6 (depends on 1) The information handling system of claim 1, wherein the iteratively sampled hardware component field performance data excludes performance data falling below a utilization threshold. ### Claim 7 (depends on 1) The information handling system of claim 1, wherein the predetermined statistical model comprises a supervised learning operation, the supervised learning operation statistically mapping a measurement of performance of the hardware component of the information handling system to an optimal hardware component configuration. ### Claim 8 A method for optimizing performance of an information handling system comprising: sampling, via a processor, hardware component testing performance measurements of a hardware component of the information handling system over a preset test monitoring duration and partitioning the hardware component testing performance measurements within the preset test monitoring duration into one or more learning windows having a frequency and a window duration preset according to each of a plurality of candidate learning window parameter configurations; applying a statistical model to the sampled hardware component testing performance measurements for each of the plurality of candidate learning window parameter configurations to yield modeled system settings; determining an accuracy associated with each candidate learning window parameter configuration by determining accuracy of each modeled system setting compared with a known optimal system setting; and identifying a most accurate window learning parameter configuration as the optimal window learning parameter configuration for use in sampling hardware component field performance measurements describing performance of a hardware component of the information handling system during field usage to determine field optimized system settings according to the statistical model using limited computing resources. ### Claim 9 (depends on 8) The method of claim 8 further comprising: characterizing workloads in operation at runtime to analyze test performance of the hardware component of the information handling system during usage; and identifying a hardware component configuration to be changed to obtain optimal hardware component field performance based upon the characterizing of workloads. ### Claim 10 (depends on 9) The method of claim 9, wherein the characterizing workloads is performed dynamically for varying workloads to account for varying workloads, the varying workloads comprising random stochastic variation in workload, or abrupt user or operating system level discontinuities in jobs. ### Claim 11 (depends on 8) The method of claim 8 further comprising: excluding from consideration as the optimal tuning parameter configuration a candidate tuning parameter configuration having a candidate accuracy falling below an accuracy threshold. ### Claim 12 (depends on 11) The method of claim 11, wherein the accuracy threshold is a false positive rate of 20%. ### Claim 13 (depends on 8) The method of claim 8, wherein the optimal learning window parameters include an optimal voting method for determination of the field optimized hardware component configurations. ### Claim 14 (depends on 8) The method of claim 8, wherein the candidate accuracy for each of the plurality of candidate tuning parameter configurations is defined by an area under a receiver operating characteristic curve for the candidate laboratory optimal system setting determination. ### Claim 15 An information handling system operating a performance optimization system comprising: a data bus coupled to a processor and a non-transitory, computer-readable storage medium; and the processor executing computer program code instructions that interact with a plurality of computer operations and that is configured for: inputting an application workload into a predetermined statistical model to output a predicted optimized hardware configuration for the information handling system; iteratively sampling hardware component field performance data describing performance of a hardware component of the information handling system during one or more learning windows, the learning windows having a preset duration and occurring at a preset frequency according to optimal learning window parameters to decrease computing resources consumed during determination of the predicted optimized hardware configuration by the predetermined statistical model, wherein the optimal learning window parameters are determined based on accuracy of previous application of the predetermined statistical model to previous hardware component test performance data as previously sampled during testing; characterizing workloads in operation at runtime based on application of the predetermined statistical model to the iteratively sampled field performance data; identifying a hardware component configuration to be changed to obtain optimal performance of the hardware component based upon the characterizing of workloads; applying the hardware component configuration to be changed to obtain optimal performance of the hardware component. ### Claim 16 (depends on 15) The information handling system of claim 15, wherein the characterizing of workloads is performed dynamically for varying workloads to account for varying workloads, the varying workloads comprising random stochastic variation in workload, or abrupt user or operating system level discontinuities in jobs. ### Claim 17 (depends on 15) The method of claim 15, wherein the instructions executable by the processor are further configured for: classifying workloads into predefined performance clusters. ### Claim 18 (depends on 17) The method of claim 17, wherein the workloads being classified comprise known workloads and unknown workloads. ### Claim 19 (depends on 15) The information handling system of claim 15, wherein the accuracy of previous application of the statistical model is defined by an area under a receiver operating characteristic curve for the previous application of the statistical model. ### Claim 20 (depends on 15) The information handling system of claim 15, wherein the optimal learning window parameters include an optimal voting method and the optimal system setting is determined based upon the optimal voting method.