Merging technique is widely adopted by I/O schedulers to maximize system I/O throughput. However, I/O merging could increase the latency of individual I/O, thus incurring prolonged I/O latencies and enlarged performance variations. Even with better system throughput, higher worst-case latency experienced by some requests could block the SSD storage system, which violates the QoS (Quality of Service) requirement. In order to improve QoS performance while providing higher I/O throughput, this paper proposes a reinforcement learning based I/O merging approach. Through learning the characteristic of various I/O patterns, the proposed approach makes merging decisions adaptively based on different I/O workloads. Evaluation results show that the proposed scheme is capable of reducing the standard deviation of I/O latency by 19.1 percent on average, worst-case latency by 7.3-60.9 percent at the 99.9th percentile compared with the latest I/O merging scheme, while maximizing system throughput.

中文翻译：

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通过基于强化学习的 SSD I/O 合并最大化 I/O 吞吐量并最小化性能变化

I/O 调度器广泛采用合并技术来最大化系统 I/O 吞吐量。但是，I/O 合并可能会增加单个 I/O 的延迟，从而导致延长的 I/O 延迟和扩大性能变化。即使具有更好的系统吞吐量，某些请求所经历的更高的最坏情况延迟也可能会阻塞 SSD 存储系统，这违反了 QoS（服务质量）要求。为了在提供更高 I/O 吞吐量的同时提高 QoS 性能，本文提出了一种基于强化学习的 I/O 合并方法。通过学习各种 I/O 模式的特性，所提出的方法可以根据不同的 I/O 工作负载自适应地做出合并决策。评估结果表明，所提出的方案能够将 I/O 延迟的标准偏差平均降低 19.1%，