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 language||English (US)|
|Title of host publication||Proceedings of the 15th USENIX Conference on File and Storage Technologies, FAST 2017|
|Number of pages||13|
|State||Published - 2019|
|Event||15th USENIX Conference on File and Storage Technologies, FAST 2017 - Santa Clara, United States|
Duration: Feb 27 2017 → Mar 2 2017
|Name||Proceedings of the 15th USENIX Conference on File and Storage Technologies, FAST 2017|
|Conference||15th USENIX Conference on File and Storage Technologies, FAST 2017|
|Period||2/27/17 → 3/2/17|
Bibliographical noteFunding 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.