用强化学习动态调优存储参数，提升系统性能。
Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques
- 基于深度Q网络实时学习I/O模式，自动调整缓存大小等参数。
- 可动态适应复杂多变的工作负载，显著缓解性能瓶颈。
- 适合需要智能自适应存储优化的云平台和数据中心。
数据密集型应用的指数级增长给现代存储系统带来了前所未有的压力，亟需动态高效的优化策略。传统启发式方法在应对当代工作负载的多样性和复杂性时往往失效，导致严重性能瓶颈和资源浪费。为此，本文提出一种基于强化学习（RL）的新型框架RL-Storage，通过深度Q-learning算法持续学习实时I/O模式，并预测最优存储参数（如缓存大小、队列深度、预读设置）。该研究凸显了强化学习在应对现代存储系统动态性方面的变革潜力。通过实时自主适应工作负载变化，RL-Storage提供了一种稳健且可扩展的存储性能优化方案，为下一代智能存储基础设施铺平道路。
原文摘要 · Abstract (English)
The exponential growth of data-intensive applications has placed unprecedented demands on modern storage systems, necessitating dynamic and efficient optimization strategies. Traditional heuristics employed for storage performance optimization often fail to adapt to the variability and complexity of contemporary workloads, leading to significant performance bottlenecks and resource inefficiencies. To address these challenges, this paper introduces RL-Storage, a novel reinforcement learning (RL)-based framework designed to dynamically optimize storage system configurations. RL-Storage leverages deep Q-learning algorithms to continuously learn from real-time I/O patterns and predict optimal storage parameters, such as cache size, queue depths, and readahead settings[1].This work underscores the transformative potential of reinforcement learning techniques in addressing the dynamic nature of modern storage systems. By autonomously adapting to workload variations in real time, RL-Storage provides a robust and scalable solution for optimizing storage performance, paving the way for next-generation intelligent storage infrastructures.
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