A Novel Resource-Driven Job Allocation Scheme for Desktop Grid Environments

  • Paolo Bertasi
  • Alberto Pettarin
  • Michele Scquizzato
  • Francesco Silvestri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6084)

Abstract

In this paper we propose a novel framework for the dynamic allocation of jobs in grid-like environments, in which such jobs are dispatched to the machines of the grid by a centralized scheduler. We apply a new, full resource-driven approach to the scheduling task: jobs are allocated and (possibly) relocated on the basis of the matching between their resource requirements and the characteristics of the machines in the grid. We provide experimental evidence that our approach effectively exploits the computational resources at hand, successfully keeping the completion time of the jobs low, even without having knowledge of the actual running times of the jobs.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abramson, D., Giddy, J., Kotler, L.: High performance parametric modeling with Nimrod/G: Killer application for the global grid? In: Proceedings of the 14th International Parallel & Distributed Processing Symposium, pp. 520–528. IEEE Computer Society, Los Alamitos (2000)Google Scholar
  2. 2.
    AEOLUS testbed website, http://aeolus.cs.upb.de
  3. 3.
    Berman, F., Wolski, R., Casanova, H., Cirne, W., Dail, H., Faerman, M., Figueira, S.M., Hayes, J., Obertelli, G., Schopf, J.M., Shao, G., Smallen, S., Spring, N.T., Su, A., Zagorodnov, D.: Adaptive computing on the grid using AppLeS. IEEE Transactions on Parallel & Distributed Systems 14(4), 369–382 (2003)CrossRefGoogle Scholar
  4. 4.
    Bertasi, P., Bianco, M., Pietracaprina, A., Pucci, G.: Obtaining performance measures through microbenchmarking in a peer-to-peer overlay computer. International Journal of Computational Intelligence Research 4(1), 1–8 (2008)CrossRefGoogle Scholar
  5. 5.
    Casanova, H., Legrand, A., Zagorodnov, D., Berman, F.: Heuristics for scheduling parameter sweep applications in grid environments. In: Proceedings of the 9th Heterogeneous Computing Workshop, pp. 349–363. IEEE Computer Society, Los Alamitos (2000)Google Scholar
  6. 6.
  7. 7.
    Feitelson, D.G., Weil, A.M.: Utilization and predictability in scheduling the IBM SP2 with backfilling. In: Proceedings of the 12th International Parallel Processing Symposium / 9th Symposium on Parallel and Distributed Processing, pp. 542–546. IEEE Computer Society, Los Alamitos (1998)CrossRefGoogle Scholar
  8. 8.
    Foster, I., Kesselman, C.: Globus: A meta-computing infrastructure toolkit. International Journal of Supercomputer Applications 11(2), 115–128 (1997)Google Scholar
  9. 9.
    Foster, I., Kesselman, C. (eds.): The Grid 2: Blueprint for a New Computing Infrastructure, 2nd edn. Morgan Kaufmann, San Francisco (2003)Google Scholar
  10. 10.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, New York (1979)MATHGoogle Scholar
  11. 11.
    Harchol-Balter, M., Downey, A.B.: Exploiting process lifetime distributions for dynamic load balancing. ACM Transactions on Computer Systems 15(3), 253–285 (1997)CrossRefGoogle Scholar
  12. 12.
    Ibarra, O.H., Kim, C.E.: Heuristic algorithms for scheduling independent tasks on nonidentical processors. Journal of the ACM 24(2), 280–289 (1977)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Khoo, B.B., Veeravalli, B., Hung, T., Simon See, C.W.: A multi-dimensional scheduling scheme in a grid computing environment. Journal of Parallel and Distributed Computing 67(6), 659–673 (2007)CrossRefMATHGoogle Scholar
  14. 14.
    Lee, C.B., Schwartzman, Y., Hardy, J., Snavely, A.: Are user runtime estimates inherently inaccurate? In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2004. LNCS, vol. 3277, pp. 253–263. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Lee, Y.C., Zomaya, A.Y.: Practical scheduling of bag-of-tasks applications on grids with dynamic resilience. IEEE Transactions on Computers 56(6), 815–825 (2007)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Leland, W., Ott, T.J.: Load-balancing heuristics and process behavior. ACM SIGMETRICS Performance Evaluation Review 14(1), 54–69 (1986)CrossRefGoogle Scholar
  17. 17.
    Lenstra, J.K., Shmoys, D.B., Tardos, É.: Approximation algorithms for scheduling unrelated parallel machines. Mathematical Programming 46, 259–271 (1990)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Litzkow, M.J., Livny, M., Mutka, M.W.: Condor – a hunter of idle workstations. In: Proceedings of the 8th International Conference on Distributed Computing Systems, pp. 104–111. IEEE Computer Society, Los Alamitos (1988)Google Scholar
  19. 19.
    Liu, C., Yang, L., Foster, I., Angulo, D.: Design and evaluation of a resource selection framework for grid applications. In: Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing, pp. 63–72. IEEE Computer Society, Los Alamitos (2002)Google Scholar
  20. 20.
    Maheswaran, M., Ali, S., Siegel, H.J., Hensgen, D., Freund, R.F.: Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. Journal Parallel and Distributed Computing 59(2), 107–131 (1999)CrossRefGoogle Scholar
  21. 21.
    Raman, R., Livny, M., Solomon, M.: Matchmaking: Distributed resource management for high throughput computing. In: Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing, pp. 140–146. IEEE Computer Society, Los Alamitos (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Paolo Bertasi
    • 1
  • Alberto Pettarin
    • 1
  • Michele Scquizzato
    • 1
  • Francesco Silvestri
    • 1
  1. 1.Department of Information EngineeringUniversity of PadovaPadovaItaly

Personalised recommendations