Adaptive Policy Generation for Storage System Performance Optimization
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Solution Overview
Problem
Backup system performance optimization is challenging due to constant changes in operational dependencies, requiring frequent manual calibration which is error-prone and inefficient, leading to underutilization or overutilization of system resources.
Innovation Solution
An automated system with a Polling Engine, Analyzer, Calibrator, and Notifier modules that monitor and adjust system resources to maintain optimal performance by polling attributes, analyzing workload, and calibrating configurations based on predefined policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration of backup environment is performed, then initial optimization can be achieved, but the optimized performance cannot be sustained due to constant environmental changes
Solution Approach 1:
The system performs self-calibration through automated polling of system attributes, analysis of workload changes, and adjustment of backup configurations without human intervention. The calibrator module automatically modifies backup software settings based on analyzed environmental changes, enabling the system to adapt to changing conditions and sustain optimized performance over time.
Solution Approach 2:
The system implements continuous feedback loops by polling system attributes (CPU utilization, memory usage, I/O throughput) and using this information to dynamically adjust backup configurations. The analyzer monitors environmental changes and feeds this information to the calibrator, which adjusts settings to maintain optimal performance despite changing conditions.
2Productivity
If constant calibration of backup software and hardware is performed to optimize performance, then optimal backup performance can be maintained, but significant manual labor and time are required
Solution Approach 1:
The system automates the entire calibration process through self-calibration, eliminating the need for manual administrator intervention. The polling engine automatically collects system attributes, the analyzer processes this data to identify environmental changes, and the calibrator adjusts configurations accordingly, freeing administrators from time-consuming manual calibration tasks.
Solution Approach 2:
The system performs preliminary analysis of system attributes and workload patterns to proactively adjust backup configurations before performance degradation occurs. By continuously monitoring CPU utilization, memory usage, and I/O throughput, the system can anticipate environmental changes and pre-adjust settings to maintain optimal performance.
3Manufacturing precision
If manual configuration examinations and adjustments are performed, then system parameters can be tuned, but error-proneness increases and maintenance overhead significantly increases
Solution Approach 1:
The system eliminates manual configuration tasks by implementing self-calibration, where the calibrator module automatically adjusts backup software settings based on analyzed environmental changes. This removes human error from the configuration process and reduces maintenance overhead by automating what would otherwise require skilled administrator intervention.
Solution Approach 2:
The analyzer acts as an intermediary between system monitoring and configuration adjustment. It processes raw system attribute data, identifies environmental changes, and translates these into appropriate configuration adjustments, removing the need for administrators to manually examine and interpret complex system parameters.
Data Source
AI summary
This disclosure relates to a method, article of manufacture, and apparatus of adaptive policy generating for storage system performance optimization. In some embodiments, this includes inspecting a storage system to obtain resources information, wherein the resources information includes attributes associated with a workload of the storage system and corresponding values, wherein the storage system includes an application configured to run a plurality of processes concurrently in an operating system producing a portion of the workload, obtaining one or more percentages, wherein the one or more percentages specifies an optimum proportion of the resources allocated to the application, obtaining amounts of the resources allocated to a process within the plurality of processes, and generating policies as a function of the resources information, the one or more percentages, and the amounts of the resources allocated to the process.