Advertisement

MetaLoRaS: A Predictable MetaScheduler for Non-dedicated Multiclusters

  • J. Ll Lérida
  • F. Solsona
  • F. Giné
  • M. Hanzich
  • P. Hernández
  • E. Luque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4330)

Abstract

The main aim of this work is to take advantage of the computer resources of a single organization or Multicluster system to execute distributed applications efficiently without excessively damaging the local users. In doing so we propose a Metascheduler environment named MetaLoRaS with a 2-level hierarchical architecture for a non-dedicated Multicluster with job prediction capabilities.

Among other Metaschedulers, the non-dedicated feature and an efficient prediction system are the most distinctive characteristics of MetaLoRaS. Another important contribution is the effective cluster selection mechanism, based on the prediction system.

In this article, we show how the hierarchical architecture and simulation mechanism are the best choices if we want to obtain an accurate prediction system and, at the same time, the best turnaround times for distributed jobs without damaging local user performance.

Keywords

Cluster State Turnaround Time Local User Parallel Application User Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abawajy, J., Dandamudi, S.: Parallel Job Scheduling on Multicluster Computing Systems. In: Proceedings of the IEEE International Conference on Cluster Computing (CLUSTER 2003) (2003)Google Scholar
  2. 2.
    Acharya, A., Setia, S.: Availability and utility of idle memory in workstation clusters. In: Proceedings of the ACM SIGM./PERF.1999, pp. 35–46 (1999)Google Scholar
  3. 3.
    Bucur, A., Epema, D.: Local versus Global Schedulers with Processor Co-allocation in Multicluster Systems. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 184–204. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Hanzich, M., Giné, F., Hernández, P., Solsona, F., Luque, E.: Coscheduling and Multiprogramming level in a non-dedicated cluster. In: Kranzlmüller, D., Kacsuk, P., Dongarra, J. (eds.) EuroPVM/MPI 2004. LNCS, vol. 3241, pp. 327–336. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Hanzich, M., Giné, F., Hernández, P., Solsona, F., Luque, E.: Cisne: A new integral approach for scheduling parallel applications on non-dedicated clusters. In: Cunha, J.C., Medeiros, P.D. (eds.) Euro-Par 2005. LNCS, vol. 3648, pp. 220–230. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Jones, W., Ligon, W., Pang, L.: Characterization of Bandwidth-Aware Meta-Schedulers for Co-Allocating Jobs Across Multiple Clusters. The Journal of Supercomputing 34, 135–163 (2005)CrossRefGoogle Scholar
  7. 7.
    Litzkow, M., Livny, M., Mutka, M.: Condor- a hunter of idle workstations. In: 8th Int’l Conference of Distributed Computing Systems (1988)Google Scholar
  8. 8.
    Bose, A., Wickman, B., Wood, C.: MARS: a metascheduler for distributed resources in campus grids. In: Proceedings. Fifth IEEE/ACM International Workshop on Grid Computing (2004)Google Scholar
  9. 9.
    Sabin, G., Kettimuthu, R., Rajan, A., Sadayappan, P.: Scheduling of Parallel Jobs in a Heterogeneous Multi-site Environment. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2003. LNCS, vol. 2862, pp. 87–104. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Santoso, J., van Albada, G.D., Nazief, B.A., Sloot, P.M.: Hierarchical Job Scheduling for Clusters of Workstations. In: Proceedings of the sixth annual conference of the Advanced School for Computing and Imaging (ASCI 2000), pp. 99–105 (2000)Google Scholar
  11. 11.
    Shmueli, E., Feitelson, D.G.: Backfilling with lookahead to optimize the performance of parallel job scheduling. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2003. LNCS, vol. 2862, pp. 228–251. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Subramani, V., Kettimuthu, R., Srinivasan, S., Sadayappan, P.: Distributed Job Scheduling on Computational Grids using Multiple Simultaneous Requests. In: Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing (2002)Google Scholar
  13. 13.
    Xu, M.: Effective Metacomputing using LSF MultiCluster, ccgrid. In: 1st International Symposium on Cluster Computing and the Grid, p. 100 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. Ll Lérida
    • 1
  • F. Solsona
    • 1
  • F. Giné
    • 1
  • M. Hanzich
    • 2
  • P. Hernández
    • 2
  • E. Luque
    • 2
  1. 1.Departamento de Informática e Ingeniería IndustrialUniversitat de LleidaSpain
  2. 2.Departamento de Arquitectura y Sistemas OperativosUniversitat Autònoma de BarcelonaSpain

Personalised recommendations