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Model building for dynamic multi-tenant provider environments

Published: 18 December 2012 Publication History

Abstract

Increasingly, storage vendors are finding it difficult to leverage existing white-box and black-box modeling techniques to build robust system models that can predict system behavior in the emerging dynamic and multi-tenant data centers. White-box models are becoming brittle because the model builders are not able to keep up with the innovations in the storage system stack, and black-box models are becoming brittle because it is increasingly difficult to a priori train the model for the dynamic and multi-tenant data center environment. Thus, there is a need for innovation in system model building area.
In this paper we present a machine learning based blackbox modeling algorithm called M-LISP that can predict system behavior in untrained region for these emerging multitenant and dynamic data center environments. We have implemented and analyzed M-LISP in real environments and the initial results look very promising. We also provide a survey of some common machine learning algorithms and how they fare with respect to satisfying the modeling needs of the new data center environments.

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Cited By

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  • (2015)Dynamic provisioning of storage workloadsProceedings of the 29th Usenix Conference on Large Installation System Administration10.5555/2907890.2907892(13-24)Online publication date: 8-Nov-2015
  • (2015)Towards Lightweight and Swift Storage Resource Management in Big Data Cloud EraProceedings of the 29th ACM on International Conference on Supercomputing10.1145/2751205.2751230(133-142)Online publication date: 8-Jun-2015
  • (2014)Queueing-based storage performance modeling and placement in OpenStack environments2014 21st International Conference on High Performance Computing (HiPC)10.1109/HiPC.2014.7116887(1-10)Online publication date: Dec-2014

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Published In

cover image ACM SIGOPS Operating Systems Review
ACM SIGOPS Operating Systems Review  Volume 46, Issue 3
December 2012
81 pages
ISSN:0163-5980
DOI:10.1145/2421648
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2012
Published in SIGOPS Volume 46, Issue 3

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Author Tags

  1. black-box
  2. machine learning
  3. resource modeling
  4. storage management

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Cited By

View all
  • (2015)Dynamic provisioning of storage workloadsProceedings of the 29th Usenix Conference on Large Installation System Administration10.5555/2907890.2907892(13-24)Online publication date: 8-Nov-2015
  • (2015)Towards Lightweight and Swift Storage Resource Management in Big Data Cloud EraProceedings of the 29th ACM on International Conference on Supercomputing10.1145/2751205.2751230(133-142)Online publication date: 8-Jun-2015
  • (2014)Queueing-based storage performance modeling and placement in OpenStack environments2014 21st International Conference on High Performance Computing (HiPC)10.1109/HiPC.2014.7116887(1-10)Online publication date: Dec-2014

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