Energy-Aware Multi-Organization Scheduling Problem

  • Johanne Cohen
  • Daniel Cordeiro
  • Pedro Luis F. Raphael
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8632)

Abstract

Scheduling algorithms for shared platforms such as grids and clouds granted users of different organizations access to powerful resources and may improve machine utilization; however, this can also increase operational costs of less-loaded organizations.

We consider energy as a resource, where the objective is to optimize the total energy consumption without increasing the energy spent by a selfish organization. We model the problem as a energy-aware variant of the Multi-Organization Scheduling Problem that we call MOSP-energy.

We show that the clairvoyant problem with variable speed processors and jobs with release dates and deadlines is NP-hard and also that being selfish can cause solutions at most mα − 1 far from the optimal, where m is the number of machines and α > 1 is a constant. Finally, we present efficient heuristics for scenarios with all jobs ready from the beginning.

References

  1. 1.
    Albers, S., Antoniadis, A., Greiner, G.: On multi-processor speed scaling with migration. In: ACM Symposium on Parallelism in Algorithms and Architectures, pp. 279–288 (2011)Google Scholar
  2. 2.
    Albers, S., Müller, F., Schmelzer, S.: Speed scaling on parallel processors. In: ACM Symposium on Parallel Algorithms and Architectures, pp. 289–298 (2007)Google Scholar
  3. 3.
    Cohen, J., Cordeiro, D., Trystram, D., Wagner, F.: Coordination mechanisms for selfish multi-organization scheduling. In: IEEE International Conference on High Performance Computing, pp. 1–9 (December 2011)Google Scholar
  4. 4.
    Cohen, J., Cordeiro, D., Trystram, D., Wagner, F.: Analysis of multi-organization scheduling algorithms. In: D’Ambra, P., Guarracino, M., Talia, D. (eds.) Euro-Par 2010, Part II. LNCS, vol. 6272, pp. 367–379. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Dutot, P.F., Pascual, F., Rzadca, K., Trystram, D.: Approximation algorithms for the multiorganization scheduling problem. IEEE Transactions on Parallel and Distributed Systems 22(11), 1888–1895 (2011)CrossRefGoogle Scholar
  6. 6.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman (January 1979)Google Scholar
  7. 7.
    Iosup, A., Dumitrescu, C., Epema, D., Li, H., Wolters, L.: How are real grids used? The analysis of four grid traces and its implications. In: 7th IEEE/ACM International Conference on Grid Computing, pp. 262–269 (September 2006)Google Scholar
  8. 8.
    Pascual, F., Rzadca, K., Trystram, D.: Cooperation in multi-organization scheduling. In: Kermarrec, A.-M., Bougé, L., Priol, T. (eds.) Euro-Par 2007. LNCS, vol. 4641, pp. 224–233. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced CPU energy. In: Symposium on Foundations of Computer Science, pp. 374–382. IEEE (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Johanne Cohen
    • 1
  • Daniel Cordeiro
    • 2
  • Pedro Luis F. Raphael
    • 2
  1. 1.Laboratoire de Recherche en Informatique (LRI, UMR 8623)Université Paris-SudOrsayFrance
  2. 2.Department of Computer ScienceUniversity of São PauloSão PauloBrazil

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