Journal of Grid Computing

, 7:537 | Cite as

Performance Prediction and Analysis of BOINC Projects: An Empirical Study with EmBOINC

  • Trilce Estrada
  • Michela Taufer
  • David P. Anderson
Open Access
Article

Abstract

Middleware systems for volunteer computing convert a set of computers that is large and diverse (in terms of hardware, software, availability, reliability, and trustworthiness) into a unified computing resource. This involves a number of scheduling policies and parameters, which have a large impact on the throughput and other performance metrics. How can we study and refine these policies? Experimentation in the context of a working project is problematic, and it is difficult to accurately model complex middleware in a conventional simulator. Instead, we use an approach in which the policies being studied are “emulated”, using parts of the actual middleware. In this paper we describe EmBOINC, an emulator based on the BOINC middleware system. EmBOINC simulates a population of volunteered clients (including heterogeneity, churn, availability, and reliability) and emulates the BOINC server components. After describing the design of EmBOINC and its validation, we present three case studies in which the impact of different scheduling policies are quantified in terms of throughput, latency, and starvation metrics.

Keywords

Volunteer Computing Docking@Home World Community Grid Simulation Emulation 

References

  1. 1.
    Anderson, D.P.: BOINC: a system for public-resource computing and storage. In: Proc. of the 5th IEEE/ACM International Workshop on Grid Computing (2004)Google Scholar
  2. 2.
    Anderson, D.P., Reed, K.: Celebrating diversity in volunteer computing. In: Proc. of the Hawaii International Conference on System Sciences (HICSS) (2009)Google Scholar
  3. 3.
    Anderson, D.P., McLeod VII, J.: Local scheduling for volunteer computing. In: Proc. of the Workshop on Large-Scale, Volatile Desktop Grids (PCGrid) (2007)Google Scholar
  4. 4.
    Brevik, J., Nurmi, D.C., Wolski, R.: Predicting bounds on queuing delay in space-shared computing environments. In: Proc. of the IEEE International Symposium on Workload Characterization (2006)Google Scholar
  5. 5.
    Casanova, H., Legrand, A., Quinson, M.: SimGrid: a generic framework for large-scale distributed experiments. In: Proc. of the 10th International Conference on Computer Modeling and Simulation (UKSIM) (2008)Google Scholar
  6. 6.
    Dominguez, P., Marques, P., Silva, L.: DGSchedSim: a trace-driven simulator to evaluate scheduling algorithms for desktop grid environments. In: Proc. of the Euromicro Conference on Parallel, Distributed, and Network-Based Processing (2006)Google Scholar
  7. 7.
    Downey, A.B.: Predicting queue times on space-sharing parallel computers. In: Proc. of the 11th International Parallel and Distributed Processing Symposium (IPDPS) (1997)Google Scholar
  8. 8.
    Estrada, T., Flores, D., Taufer, M., Teller, P., Kerstens, A., Anderson, D.P.: The effectiveness of threshold-based scheduling policies in BOINC projects. In: Proc. of the 2nd IEEE International Conference in e-Science and Grid Computing (e-Science) (2006)Google Scholar
  9. 9.
    Estrada, T., Taufer, M., Reed, K.: Modeling job lifespan delays in volunteer computing projects. In: Proc. of the 9th IEEE International Symposium on Cluster Computing and Grid (CCGrid) (2009)Google Scholar
  10. 10.
    Estrada, T., Taufer, M., Reed, K., Anderson, D.P.: EmBOINC: an emulator for performance analysis of BOINC projects. In: Proc. of the 3rd Workshop on Desktop Grids and Volunteer Computing Systems (PCGrid) (2009)Google Scholar
  11. 11.
    Gathmann, F.O.: Python as a discrete event simulation environment. In: Proc. of the 7th International Python Conference (1998)Google Scholar
  12. 12.
    Heien, E.M., Fujimoto, N., Hagihara, K.: Computing low latency batches with unreliable workers in volunteer computing environments. In: Proc. of the 22nd International Parallel and Distributed Processing Symposium (IPDPS) (2008)Google Scholar
  13. 13.
    Ingalls, R.: Introduction to simulation. In: Proc. of the 2002 Winter Simulation Conference (2002)Google Scholar
  14. 14.
    Iverson, M.A., Ozguner, F., Potter, L.: Statistical prediction of task execution times through analytic benchmarking for scheduling in a heterogeneous environment. IEEE Trans. Comput. 48(12) 1374–1379 (1999)CrossRefGoogle Scholar
  15. 15.
    Kondo, D., Anderson, D.P., McLeod VII, J.: Performance evaluation of scheduling policies for volunteer computing. In: Proc. of the 3rd IEEE International Conference on e-Science and Grid Computing (e-Science) (2007)Google Scholar
  16. 16.
    Mahadevan, P., Rodriguez, A., Becker, D., Vahdat, A.: MobiNet: a scalable emulation infrastructure for ad hoc and wireless networks. In: Proc. of the International Conference on Mobile Systems, Applications and Services (2005)Google Scholar
  17. 17.
    Mutka, M.W., Livny, M.: Profiling workstations’ available capacity for remote execution. In: Proc. of the 12th International Symposium on Computer Performance Modeling, Measurement and Evaluation (1988)Google Scholar
  18. 18.
    Nurmi, D., Mandal, A., Brevik, J., Koelbel, C., Wolski, R., Kennedy, K.: Evaluation of a workflow scheduler using integrated performance modelling and batch queue wait time prediction. In: Proc. of the International Conference for High Performance Computing, Networking, Storage, and Analysis (2006)Google Scholar
  19. 19.
    Nurmi, D.C., Brevik, J., Wolski, R.: Qbets: queue bounds estimation from time series. In: Proc. of the 2007 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (2007)Google Scholar
  20. 20.
    Schriber, T.J., Brunner, D.T.: Inside discrete-event simulation software. In: Proc. of the 2003 Winter Simulation Conference (2003)Google Scholar
  21. 21.
    Shannon, R.E.: Introduction to the art and science of simulation. In: Proc. of the 1998 Winter Simulation Conference (1998)Google Scholar
  22. 22.
    Smith, W., Taylor, V., Foster, I.: Using run-time predictions to estimate queue wait times and improve scheduler performance. In: Job Scheduling Strategies for Parallel Processing, pp. 202–219. Springer, New York (1999)CrossRefGoogle Scholar
  23. 23.
    Taufer, M., Anderson, D.P., Cicotti, P., Brooks III, C.L.: Homogeneous redundancy: a technique to ensure integrity of molecular simulation results using public computing. In: Proc. of the 14th Heterogeneous Computing Workshop (2005)Google Scholar
  24. 24.
    Taufer, M., An, C., Kerstens, A., Brooks III, C.L.: Predictor@home: a protein structure prediction supercomputer based on global computing. IEEE Trans. Parallel Distrib. Syst. 17(8), 786–796 (2006)CrossRefGoogle Scholar
  25. 25.
    Wolski, R., Nurmi, D., Brevik, J., Casanova, H., Chien, A.: Models and modeling infrastructures for global computational platforms. In: Proc. of the 22nd International Parallel and Distributed Processing Symposium (IPDPS) (2005)Google Scholar
  26. 26.
    Wolski, R., Nurmi, D., Brevik, J.: An analysis of availability distributions in condor. In: Proc. of the 21st International Parallel and Distributed Processing Symposium (IPDPS) (2007)Google Scholar
  27. 27.
    Xia, H., Dail, H., Casanova, H., Chien, A.: The MicroGrid: using emulation to predict application performance in diverse grid network environments. In: Proc. of the Workshop on Challenges of Large Applications in Distributed Environments (2004)Google Scholar

Copyright information

© The Author(s) 2009

Authors and Affiliations

  • Trilce Estrada
    • 1
  • Michela Taufer
    • 1
  • David P. Anderson
    • 2
  1. 1.University of DelawareNewarkUSA
  2. 2.U.C. Berkeley Space Sciences LaboratoryBerkeleyUSA

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