8. Toward Energy-Aware Scheduling Using Machine Learning

  1. Albert Y. Zomaya and
  2. Young Choon Lee
  1. Josep Ll. Berral1,
  2. Iñigo Goiri2,
  3. Ramon Nou2,
  4. Ferran Julià2,
  5. Josep O. Fitó2,
  6. Jordi Guitart2,
  7. Ricard Gavaldá1 and
  8. Jordi Torres2

Published Online: 30 JUL 2012

DOI: 10.1002/9781118342015.ch8

Energy-Efficient Distributed Computing Systems

Energy-Efficient Distributed Computing Systems

How to Cite

Berral, J. Ll., Goiri, I., Nou, R., Julià, F., Fitó, J. O., Guitart, J., Gavaldá, R. and Torres, J. (2012) Toward Energy-Aware Scheduling Using Machine Learning, in Energy-Efficient Distributed Computing Systems (eds A. Y. Zomaya and Y. C. Lee), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118342015.ch8

Editor Information

  1. Centre for Distributed and High Performance Computing, School of Information Technologies, University of Sydney, NSW, Australia

Author Information

  1. 1

    Computer Architecture Dept. and Department of Software, UPC-Barcelona Tech., Catalonia, Spain

  2. 2

    Computer Architecture Dept. and Barcelona Supercomputing Center, UPC-Barcelona Tech., Catalonia, Spain

Publication History

  1. Published Online: 30 JUL 2012
  2. Published Print: 3 AUG 2012

ISBN Information

Print ISBN: 9780470908754

Online ISBN: 9781118342015

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Keywords:

  • energy-aware scheduling using machine learning;
  • cloud, and applications, creating hard optimization problems;
  • optimal performance/intelligent management for data centers;
  • machine learning techniques, supervised/unsupervised learning;
  • green computing and energy efficiency, virtualization, DVFS, hybrid

Summary

This chapter contains sections titled:

  • Introduction

  • Intelligent Self-Management

  • Introducing Power-Aware Approaches

  • Experiences of Applying ML on Power-Aware Self-Management

  • Conclusions on Intelligent Power-Aware Self-Management

  • References