An Architecture for Resource Behavior Prediction to Improve Scheduling Systems Performance on Enterprise Desktop Grids

  • Sergio Ariel Salinas
  • Carlos García Garino
  • Alejandro Zunino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7547)

Abstract

An Enterprise Desktop Grid (EDG) is a low cost platform that scavenges idle desktop computers to run Grid applications. Since EDGs use idle computer time, it is important to estimate the expected computer availability. Based on this estimation, a scheduling system is able to select those computers with more expected availability to run applications. As a consequence, an overall performance improvement is achieved. Different techniques have been proposed to predict the computer state for an instant of time, but this information is not enough. A prediction model provides a sequence of computer states for different instants of time. The problem is how to identify computer behavior having as input this sequence of states. We identify the need of providing a architecture to model and evaluate desktop computer behavior. Thus, a scheduling system is able to compare and select resources that run applications faster. Experiments have shown that programs run up to 8 times faster when the scheduler selects a computer suggested by our proposal.

Keywords

Enterprise Desktop Grid Resource Discovery System Computer Behavior Prediction Scheduling System Classification Algorithms 

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References

  1. 1.
    Choi, S., et al.: Characterizing and classifying desktop grid. In: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid, CCGRID 2007, pp. 743–748. IEEE Computer, Washington (2007)CrossRefGoogle Scholar
  2. 2.
    Brent, C., et al.: Planetlab: an overlay testbed for broad-coverage services. ACM SIGCOMM Computer Communication Review 33, 3–12 (2003)CrossRefGoogle Scholar
  3. 3.
    Dinda, P.: The statistical properties of host load. Sci. Program. 7, 211–229 (1999)Google Scholar
  4. 4.
    Dinda, P.: Online prediction of the running time of tasks, vol. 5, pp. 225–236. Kluwer Academic Publishers, Hingham (2002)Google Scholar
  5. 5.
    Dinda, P.: A prediction-based real-time scheduling advisor. In: Proc. International Parallel and Distributed Processing Symposium, IPDPS 2002, Abstracts and CD-ROM, pp. 10–17 (2002)Google Scholar
  6. 6.
    Dobber, M., Koole, G., van der Mei, R.: Dynamic load balancing experiments in a grid. In: Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2005), vol. 2, pp. 1063–1070. IEEE Press, New York (2005)CrossRefGoogle Scholar
  7. 7.
    Edwards, W.: Discovery systems in ubiquitous computing. IEEE Pervasive Computing 5(2), 70–77 (2006)CrossRefGoogle Scholar
  8. 8.
    Kang, W., Grimshaw.: Failure prediction in computational grids. In: 40th Annual Simulation Symposium, ANSS 2007, pp. 275–282 (2007)Google Scholar
  9. 9.
    Meshkova, E., et al.: A survey on resource discovery mechanisms, peer-to-peer and service discovery frameworks. Computer Networks 52(11), 2097–2128 (2008)CrossRefGoogle Scholar
  10. 10.
    Weka Machine Learning Project. Weka, http://www.cs.waikato.ac.nz
  11. 11.
    Ramachandran, K., Lutfiyya, H., Perry, M.: Decentralized approach to resource availability prediction using group availability in a p2p desktop grid. Future Generation Computer Systems (2010)Google Scholar
  12. 12.
    Salzberg, S.: C4.5: Programs for machine learning by Quinlan, R. Morgan Kaufmann. Machine Learning 16, 235–240 (1994)MathSciNetGoogle Scholar
  13. 13.
    Trunfio, D., et al.: Peer-to-peer resource discovery in grids: Models and systems. Future Generation Computer Systems 23(7), 864–878 (2007)CrossRefGoogle Scholar
  14. 14.
    Vanthournout, K., Deconinck, G., Belmans, R.: A taxonomy for resource discovery. Personal and Ubiquitous Computing 9, 81–89 (2005)CrossRefGoogle Scholar
  15. 15.
    Witten, I., et al.: Weka: Practical machine learning tools and techniques with java implementations (1999)Google Scholar
  16. 16.
    Wolski, R.: Experiences with predicting resource performance on-line in computational grid settings. Sigmetrics Perform. Eval. Rev. 30, 41–49 (2003)CrossRefGoogle Scholar
  17. 17.
    Wolski, R., Spring, N., Hayes, J.: The network weather service: a distributed resource performance forecasting service for metacomputing. Future Generation Computer Systems 15(5-6), 757–768 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sergio Ariel Salinas
    • 1
  • Carlos García Garino
    • 1
    • 2
  • Alejandro Zunino
    • 3
  1. 1.Instituto para las Tecnologías de la Información y las Comunicaciones (ITIC)UNCuyoMendozaArgentina
  2. 2.Facultad de IngenieríaUNCuyoMendozaArgentina
  3. 3.ISISTAN, Facultad de Ciencias ExactasUNICENTandilArgentina

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