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Autonomic Storage Management at Scale

Published: 17 April 2017 Publication History

Abstract

Cloud data centers use enormous amounts of storage, and it is critical to monitor, manage, and optimize the storage autonomically configuring storage is difficult because storage workloads are very diverse and change over time. Data centers measure running workloads, but this measurement data stream is itself quite large. We present some real world case studies in the use of big data techniques, sampling, and optimization to manage storage in data centers.

References

[1]
C. Albrecht et al. Janus: Optimal flash provisioning for cloud storage workloads. Proc. USENIX Annual Technical Conference, pages 91--102, June 2013, San Jose, CA.
[2]
A. Sharov et al. Take me to your leader!: Online optimization of distributed storage configurations. Proc. VLDB Endow., 8(12):1490--1501, Aug. 2015.
[3]
M Möstl et al. Self-aware systems for the internet-of-things Proc. Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES), October 2016.

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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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2017

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Author Tags

  1. data center management
  2. monitoring
  3. sampling

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ICPE '17
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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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