Cheng Ji, Lun Wang, Qiao Li, Congming Gao, Liang Shi, Chia-Lin Yang, C. Xue
{"title":"Fair Down to the Device: A GC-Aware Fair Scheduler for SSD","authors":"Cheng Ji, Lun Wang, Qiao Li, Congming Gao, Liang Shi, Chia-Lin Yang, C. Xue","doi":"10.1109/NVMSA.2019.8863523","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":438544,"journal":{"name":"2019 IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)","volume":"34 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NVMSA.2019.8863523","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
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.