A Model for Client Recommendation to a Desktop Grid Server

  • Mohammad Yaser Shafazand
  • Rohaya Latip
  • Azizol Abdullah
  • Masnida Hussin
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)


A vast amount of idle computational power of desktop com- puters could be utilized throughout desktop grids. For an appropriate utilization, the scheduler, needs to determine clients which are best suited to deliver assigned jobs in time. Diversity of hosts (i.e. OS, hardware and network speci_cations) and intermittent availability of resources are known issues which complicate the schedulers work. As a solution to this problem, a client–server model consisting two modules for a desktop grid middleware is discussed: a module to forecast machine resource availability in the client side and a module in the server side that recommends clients to the scheduler that are the nearest to job expectations. Historic data, time-series analyses and machine learning are used for this purpose in the modules.


Desktop grid Availability prediction Case-based reasoning Match-making Recommender system 


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  1. 1.
    D. L_azaro, D. Kondo, and J. M. Marqu_es, Long-term availability prediction for groups of volunteer resources, Journal of Parallel and Distributed Computing, vol. 72, no. 2, pp.281296, 2012.Google Scholar
  2. 2.
    B. Rood and M. J. Lewis, Multi-state grid resource availability characterization, in Proceedings of the 8th IEEE/ACM International Conference on Grid Computing, pp.4249, 2007.Google Scholar
  3. 3.
    T. Tannenbaum, D. Wright, K. Miller, and M. Livny, Condor {A Distributed Job Scheduler, in in Beowulf Cluster Computing with Linux, T.Sterling, Ed. MIT Press, 2002.Google Scholar
  4. 4.
    M. Wu and X.-H. Sun, Grid harvest service: A performance system of grid comput- ing, Journal of Parallel and Distributed Computing, vol. 66, no. 10, pp. 13221337, 2006.Google Scholar
  5. 5.
    L. N. Nassif, J. M. Nogueira, A. Karmouch, M. Ahmed, and F. V. de Andrade, Job completion prediction using case-based reasoning for Grid computing environ- ments, Concurrency and Computation: Practice and Experience, vol. 19, no. 9, pp. 12531269, 2007.Google Scholar
  6. 6.
    R. Wolski, Dynamically forecasting network performance using the Network Weather Service, Cluster Computing, vol. 1, no. 1, pp. 119132, 1998.Google Scholar
  7. 7.
    B. Javadi, D. Kondo, J.-M. Vincent, and D. P. Anderson, Discovering Statisti- cal Models of Availability in Large Distributed Systems: An Empirical Study of SETI@home, Parallel and Distributed Systems, IEEE Transactions on, vol. 22, no. 11, pp. 18961903, 2011.Google Scholar
  8. 8.
    K. Singh, E. _Ipek, S. A. McKee, B. R. de Supinski, M. Schulz, and R. Caruana, Predicting parallel application performance via machine learning approaches, Con- currency and Computation: Practice and Experience, vol. 19, no. 17, pp. 22192235, 2007.Google Scholar
  9. 9.
    L. Hu, X.-L. Che, and S.-Q. Zheng, Online System for Grid Resource Monitoring and Machine Learning-Based Prediction, Parallel and Distributed Systems, IEEE Transactions on, vol. 23, no. 1, pp. 134145, 2012.Google Scholar
  10. 10.
    E. Xia, I. Jurisica, J. Waterhouse, and V. Sloan, Runtime Estimation Using the Case-Based Reasoning Approach for Scheduling in a Grid Environment, in Case- Based Reasoning. Research and Development, vol. 6176, I. Bichindaritz and S. Montani, Eds. Springer Berlin/Heidelberg, pp. 525539, 2010.Google Scholar
  11. 11.
    I. Watson, Case-based reasoning is a methodology not a technology, Knowledge- Based Systems, vol. 12, no. 56, pp. 303308, 1999.Google Scholar
  12. 12.
    A. Aamodt, Case-based reasoning: Foundational issues, methodological variations, and system approaches, AI communications, vol. 7, pp. 3959, 1994.Google Scholar
  13. 13.
    J. A. Recio-Garc__a, P. A. Gonzlez-Calero, and B. Daz-Agudo, jcolibri2: A frame- work for building Case based reasoning systems, Science of Computer Programming, no.0, p. -, 2012.Google Scholar
  14. 14.
    D. Kondo, B. Javadi, A. Iosup, and D. Epema, The Failure Trace Archive: Enabling Comparative Analysis of Failures in Diverse Distributed Systems, in Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on, pp. 398407, 2010.Google Scholar
  15. 15.
    G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time series analysis: forecasting and control. Prentice Hall, Englewood Cli_s, NJ, USA, 3rd edition edition, 1994, p. 598.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Mohammad Yaser Shafazand
    • 1
  • Rohaya Latip
    • 1
  • Azizol Abdullah
    • 1
  • Masnida Hussin
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdangMalaysia

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