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abstract

PerfExt++: Performance Extrapolation of IO Intensive Workloads

Published: 17 April 2017 Publication History

Abstract

We present a tool, PerfExt++, for cross platform performance extrapolation of IO intensive workloads. The tool is based on trace and replay mechanism and has the capability to record and replay the temporal and spatial characteristics of IO workloads. We show the design and implementation of PerfExt++, which requires minimal intervention from the user to predict and extrapolate performance metrics across platforms.

References

[1]
D. Chahal, R. Virk, and M. Nambiar. Performance extrapolation of io intensive workloads: Work in progress. In Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering, ICPE '16, pages 105--108, New York, NY, USA, 2016. ACM.
[2]
S. Duttagupta and R. Mansharamani. Extrapolation tool for load testing results. In Performance Evaluation of Computer Telecommunication Systems (SPECTS) 2011 International Symposium on, pages 69--76, June 2011.
[3]
ioreplay. http://code.google.com/p/ioapps/wiki/ioreplay, 2016. Online; accessed-2016-08-06.

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  1. PerfExt++: Performance Extrapolation of IO Intensive Workloads

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    cover image ACM Conferences
    ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering
    April 2017
    450 pages
    ISBN:9781450344043
    DOI:10.1145/3030207
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 17 April 2017

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    Author Tags

    1. extrapolation
    2. io traces
    3. performance prediction

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    ICPE '17 Paper Acceptance Rate 27 of 83 submissions, 33%;
    Overall Acceptance Rate 252 of 851 submissions, 30%

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