Reinforcement Learning Storage IO Load Testing
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Solution Overview
Problem
Existing storage system testing methods face challenges in finding the most effective load change mode due to varying hardware and software configurations, leading to inefficient stress testing and potential missed vulnerabilities.
Innovation Solution
A reinforcement learning-based method and electronic device that acquire and update IO load information to identify the most impactful IO load changes, optimizing storage performance testing by dynamically adjusting load configurations based on empirical values and preset conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing with different storage IO workloads is performed to find vulnerabilities, then vulnerability detection capability is improved, but testing efficiency deteriorates due to the difficulty of finding the most effective load change mode for each storage system
Solution Approach 1:
The testing system performs self-learning through reinforcement learning, automatically identifying the most effective IO workload combinations for stress testing without requiring manual configuration expertise. The system learns from testing results and autonomously optimizes future testing strategies, making the testing process self-improving and eliminating dependency on manual configuration skills
Solution Approach 2:
The system implements feedback mechanisms where testing results are continuously analyzed and fed back into the reinforcement learning model. This feedback loop allows the system to learn from previous testing outcomes and adjust IO workload configurations accordingly, progressively improving both vulnerability detection effectiveness and testing efficiency over time
2Productivity
If reinforcement learning is used to automatically find the most effective load combination change mode, then testing efficiency is improved, but device complexity increases due to the need for learning algorithms and data processing
Solution Approach 1:
The reinforcement learning model serves multiple functions: it selects IO workload configurations, predicts system responses, optimizes stress testing strategies, and adapts to different storage system configurations. This multi-functionality reduces the need for separate specialized tools and manual processes, justifying the added complexity through consolidated capabilities
Solution Approach 2:
The system dynamically adjusts IO workload parameters such as read/write ratios, block sizes, and concurrency levels based on reinforcement learning decisions. By automatically optimizing these parameters rather than using fixed configurations, the system achieves superior testing efficiency that outweighs the complexity of implementing adaptive parameter control
Data Source
AI summary
Techniques for storage testing involve: acquiring a first state of a storage system including first input/output (IO) load information; taking a first action based on the first state, the first action causing the first IO load information to be changed to second IO load information; updating the first action to be a reserved action for the first state if it is obtained based on the second IO load information that the storage system reaches a preset condition; and obtaining an action combination of a plurality of IO load information changes based on a plurality of reserved actions corresponding to a plurality of states, wherein the plurality of states include the first state. Accordingly, the most effective load combination change mode for the storage system can be found automatically and more accurately, so as to find more vulnerabilities of the storage system, thereby improving the efficiency of storage system testing.