公平到设备:一个GC-Aware公平调度的SSD

Cheng Ji, Lun Wang, Qiao Li, Congming Gao, Liang Shi, Chia-Lin Yang, C. Xue
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引用次数: 2

摘要

固态硬盘(SSD)是当前海量数据存储的主流解决方案。对于现代计算机系统来说,公平的资源分配是一个重要的设计考虑因素,近年来引起了人们的极大兴趣。尽管在主机端为ssd提供了几个I/O公平性调度器,但是如果在设备端触发垃圾收集(GC),进程公平性仍然会显著降低。GC操作可能会阻塞I/O请求,这会导致不可预测的读/写延迟变化,并进一步影响进程之间的公平性。本文提出了一种新的主机和设备协同I/O调度策略Fair-GC,以实现真正的GC干扰。关键思想是小心地编排ssd内的GC操作,以便GC对进程性能的损害程度与单独运行时相同(或相当)。通过这种方式,可以在存在GC的情况下维护主机端调度器维护的I/O公平性。此外,我们的调度器确保在主机端调度器中维护的进程的时间片及时更新,以避免为了维护公平性而造成不必要的减速。在各种工作负载下的实验结果表明,该技术在实现公平性的同时显著提高了吞吐量。与传统的基于公平性的I/O调度器相比,Fair-GC可以将实际应用程序的速度降低99%,并将吞吐量提高225%。
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Fair Down to the Device: A GC-Aware Fair Scheduler for SSD
Solid-state drives (SSD) are the mainstream solutions for massive data storage today. For modern computer systems, fair resource assignment is a critical design consideration and has drawn great interests in recent years. Although there are several I/O fairness schedulers proposed on the host side for SSDs, process fairness could still be dramatically degraded if garbage collection (GC) is triggered in the device side. A GC operation could block I/O requests, which causes unpredictable read/write latency variation and further impacts fairness between processes. This paper proposes Fair-GC, a novel coordinated host and device I/O scheduling strategy to achieve true fairness considering GC interferences. The key idea is to orchestrate GC operations inside SSDs carefully such that performance of a process is penalized by GC in the same degree (or comparable) as when it runs alone. In this way, the I/O fairness maintained by the host-side scheduler can be maintained in the presence of GC. Furthermore, our scheduler ensures that the timeslice of a process maintained at the host-side scheduler is updated in a timely manner to avoid unnecessary slowdown for maintaining fairness. Experimental results with a wide range of workloads verify that the proposed technique can achieve fairness as well as improve the throughput significantly. Compared to conventional fairness-based I/O scheduler, Fair-GC can reduce the slowdown of real applications by up to 99%, and improve the throughput by as much as 225%, respectively.
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