On the Accuracy and Scalability of Intensive I/O Workload Replay
Date
February 25, 2017
Author
Alireza Haghdoost and Weiping He, University of Minnesota; Jerry Fredin, NetApp; David H.C. Du, University of Minnesota
15th USENIX Conference on File and Storage Technologies (FAST 2017) Feb. 27 - March 2, 2017 Santa Clara, CA
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.
Resources
The definitive version of the paper can be found at: https://www.usenix.org/conference/fast17/technical-sessions/presentation/haghdoost.
