Read disturb is a circuit-level noise in solid-state drives (SSDs), which may corrupt existing data in SSD blocks and then cause high read error rate and longer read latency. The approach of read refresh is commonly used to avoid read disturb errors by periodically migrating the hot read data to other free blocks, but it places considerable negative impacts on I/O (Input/Output) responsiveness. This article proposes scheduling approaches on write data and read refresh operations, to mitigate the negative effects caused by read disturb. To be specific, we first construct a model to classify SSD blocks into two categories according to the estimated read error rate by referring to the factors of block’s P/E (Program/Erase) cycle and the accumulated read count to the block. Then, the data being intensively read will be redirected to the block having a small read error rate, as it is not sensitive to read disturb even though the data will be heavily requested. Moreover, we take advantage of reinforcement learning to predict the idle interval between two I/O requests for purposely conducting (partial) read refresh operations. As a result, it is able to minimize negative impacts toward subsequent incoming I/O requests and to ensure I/O responsiveness. Through a series of emulation tests on several realistic disk traces, we demonstrate that the proposed mechanisms can noticeably yield performance improvements on the metrics of read error rate and I/O latency.

中文翻译：

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减轻 SSD 中读取干扰的负面影响

读取干扰是固态驱动器 (SSD) 中的一种电路级噪声，它可能会破坏 SSD 块中的现有数据，然后导致高读取错误率和更长的读取延迟。读取刷新的方法通常用于通过定期将热读取数据迁移到其他空闲块来避免读取干扰错误，但它对 I/O（输入/输出）响应能力产生了相当大的负面影响。本文提出了写数据和读刷新操作的调度方法，以减轻读干扰带来的负面影响。具体来说，我们首先参考block的P/E（Program/Erase）周期和对block的累计读计数等因素，构建了一个模型，根据预估的读错误率将SSD block分为两类。然后，被密集读取的数据将被重定向到具有较小读取错误率的块，因为即使数据将被大量请求，它对读取干扰也不敏感。此外，我们利用强化学习来预测两个 I/O 请求之间的空闲间隔，以便有目的地进行（部分）读取刷新操作。因此，它能够最大限度地减少对后续传入 I/O 请求的负面影响，并确保 I/O 响应能力。通过对几个实际磁盘跟踪的一系列仿真测试，我们证明了所提出的机制可以显着提高读取错误率和 I/O 延迟指标的性能。我们利用强化学习来预测两个 I/O 请求之间的空闲间隔，以便有目的地进行（部分）读取刷新操作。因此，它能够最大限度地减少对后续传入 I/O 请求的负面影响，并确保 I/O 响应能力。通过对几个真实磁盘跟踪的一系列仿真测试，我们证明了所提出的机制可以显着提高读取错误率和 I/O 延迟指标的性能。我们利用强化学习来预测两个 I/O 请求之间的空闲间隔，以便有目的地进行（部分）读取刷新操作。因此，它能够最大限度地减少对后续传入 I/O 请求的负面影响，并确保 I/O 响应能力。通过对几个真实磁盘跟踪的一系列仿真测试，我们证明了所提出的机制可以显着提高读取错误率和 I/O 延迟指标的性能。