Performance Optimization System Using Periodic Learning Windows
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Solution Overview
Problem
Existing information handling systems face challenges in dynamically optimizing performance based on workload analysis during execution, as they rely on static benchmark and workload analysis performed in test environments, failing to adapt to varying and complex workloads in real-time.
Innovation Solution
A performance optimization system utilizing a statistical model, such as a machine learning model, to analyze instrumentation data during workload execution, dynamically adjusting system settings to optimize performance while minimizing resource dedication for data collection, thereby balancing accuracy and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous instrumentation data collection is performed to improve workload analysis accuracy, then measurement precision is improved, but resource consumption and system lag increase
Solution Approach 1:
The system implements periodic learning windows at predetermined intervals instead of continuous data collection. The machine learning model processes instrumentation data in discrete periodic batches, allowing the system to balance measurement precision with resource conservation by collecting and analyzing data only at specific time intervals rather than continuously
Solution Approach 2:
The system collects and processes only a subset of instrumentation data during learning windows rather than all available data continuously. By selecting representative samples and processing partial data sets during periodic learning windows, the system achieves sufficient workload analysis accuracy while significantly reducing computational resource consumption
2Measurement precision
If learning window duration is increased to improve model accuracy, then measurement precision is improved, but productivity decreases due to longer data collection periods
Solution Approach 1:
The system uses periodic learning windows with predetermined durations and intervals, allowing the model to be updated at regular intervals rather than requiring long continuous data collection periods. This periodic approach enables the system to maintain model accuracy while preserving productivity by limiting data collection to specific time windows
Solution Approach 2:
The system dynamically adjusts the balance between learning window duration and frequency based on workload characteristics. By making the learning window parameters adaptive rather than fixed, the system can extend data collection when accuracy is prioritized and reduce collection duration when responsiveness is more critical, optimizing the trade-off between model accuracy and system productivity
3Adaptability or versatility
If frequent learning windows are implemented to improve real-time optimization, then adaptability is improved, but resource consumption increases
Solution Approach 1:
The system implements learning windows at predetermined periodic intervals rather than continuously or on-demand, enabling the model to adapt to changing workloads at regular intervals. This periodic implementation provides real-time optimization capability while controlling resource consumption by limiting model updates to specific time points rather than continuously
Solution Approach 2:
The system changes the frequency and duration parameters of learning windows based on system state and workload characteristics. By dynamically adjusting these parameters, the system can increase adaptability during periods of high workload variation while reducing resource consumption during stable conditions, optimizing the balance between real-time optimization and resource usage
Data Source
AI summary
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.