On the accuracy and scalability of intensive I/O workload replay

Alireza Haghdoost, Weiping He, Jerry Fredin, David H.C. Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

We introduce a replay tool that can be used to replay captured I/O workloads for performance evaluation of high-performance storage systems. We study several sources in the stock operating system that introduce the uncertainty of replaying a workload. Based on the remedies of these findings, we design and develop a new replay tool called hfplayer that can more accurately replay intensive block I/O workloads in a similar unscaled environment. However, to replay a given workload trace in a scaled environment, the dependency between I/O requests becomes crucial. Therefore, we propose a heuristic way of speculating I/O dependencies in a block I/O trace. Using the generated dependency graph, hfplayer is capable of replaying the I/O workload in a scaled environment. We evaluate hfplayer with a wide range of workloads using several accuracy metrics and find that it produces better accuracy when compared with two exiting available replay tools.

Original languageEnglish (US)
Title of host publicationProceedings of the 15th USENIX Conference on File and Storage Technologies, FAST 2017
PublisherUSENIX Association
Pages315-327
Number of pages13
ISBN (Electronic)9781931971362
StatePublished - 2019
Event15th USENIX Conference on File and Storage Technologies, FAST 2017 - Santa Clara, United States
Duration: Feb 27 2017Mar 2 2017

Publication series

NameProceedings of the 15th USENIX Conference on File and Storage Technologies, FAST 2017

Conference

Conference15th USENIX Conference on File and Storage Technologies, FAST 2017
CountryUnited States
CitySanta Clara
Period2/27/173/2/17

Bibliographical note

Funding Information:
We thank our shepherd, Remzi Arpaci-Dusseau and Matias Bjørling, and the anonymous reviewers for their comments and suggestions. This work has been supported by NSF I/UCRC Center for Research in Intelligent Storage (CRIS) and the National Science Foundation (NSF) under awards 130523, 1439622, and 1525617 as well as the support from NetApp.

Publisher Copyright:
© Proceedings of the 15th USENIX Conference on File and Storage Technologies, FAST 2017. All rights reserved.

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