A New Approach for Buffering Space in Scheduling Unknown Service Time Jobs in a Computational Cluster with Awareness of Performance and Energy Consumption

  • Xuan T. Tran
  • Binh T. Vu
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 282)


In this paper, we present a new approach concentrating on buffering schemes along with scheduling policies for distribution of compute – intensive jobs with unknown service times in a cluster of heterogeneous servers. We utilize two types of ADM Operton processors of which parameters are measured according to SPEC’s Benchmark and Green500 list. We investigate three cluster models according to buffering schemes (server-level queue, class-level queue, and cluster-level queue). The simulation results show that the buffering schemes significantly influence the performance capacity of clusters, regarding the waiting time and response time experienced by incoming jobs while they retain energy efficiency of system.


heterogeneous resource computational grid scheduling policy buffering scheme server -level class -level cluster -level 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    El-Rewini, H., Lewis, T., Ali, H.: Task Scheduling in Parallel and distributed Systems. Prentice Hall, Englewood Cliffs (1994)Google Scholar
  2. 2.
    Gkoutioudi, K.Z., Karatza, H.D.: Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm. Journal of Grid Computing, 311–323 (March 2012)Google Scholar
  3. 3.
    Yagoubi, B., Slimani, Y.: Dynamic load balancing strategy for grid computing. World Academy of Science, Engineering and Technology 19 (2006)Google Scholar
  4. 4.
    Terzopoulos, G., Karatza, H.D.: Performance evaluation of a real-time grid system using power-saving capable processors. The Journal of Supercomputing, 1135–1153 (2012)Google Scholar
  5. 5.
    Zikos, S., Karatza, H.D.: Communication cost effective scheduling policies of nonclairvoyant jobs with load balancing in a grid. Journal of Systems and Software, 2103–2116 (2009)Google Scholar
  6. 6.
    Zikos, S.: Helen D. Karatza: A clairvoyant site allocation policy based on service demands of jobs in a computational grid. Simulation Modelling Practice and Theory, 1465–1478 (2011)Google Scholar
  7. 7.
    Zikos, S., Karatza, H.D.: The impact of service demand variability on source allocation strategies in a grid system. ACM Trans. Model. Comput. Simul., 19:1–19:29 (November 2010)Google Scholar
  8. 8.
    He, Y., Hsu, W., Leiserson, C.: Provably efficient online non-clairvoyant adaptive scheduling, pp. 1–10 (March 2007)Google Scholar
  9. 9.
    Wang, T., Zhou, X.-S., Liu, Q.-R., Yang, Z.-Y., Wang, Y.-L.: An Adaptive Resource Scheduling Algorithm for Computational Grid, pp. 447–450 (December 2006)Google Scholar
  10. 10.
    Opitz, A., König, H., Szamlewska, S.: What does grid computing cost? Journal of Supercomputing, 385–397 (2008)Google Scholar
  11. 11.
    Kaur, P., Singh, H.: Adaptive dynamic load balancing in grid computing an approach. International Journal of Engineering Science and Advance Technology (IJESAT), 625–632 (June 2012)Google Scholar
  12. 12.
    Chedid, W., Yu, C., Lee, B.: Power analysis and optimization techniques for energy efficient computer systemsGoogle Scholar
  13. 13.
    Zhuo, L., Liang, A., Xiao, L., Ruan, L.: Workload – aware Power Management of Cluster Systems, pp. 603–608 (August 2010)Google Scholar
  14. 14.
    Chedid, W., Yu, C.: Survey on Power Management Techniques for Energy Efficient Computer Systems, (retrieved)
  15. 15.
    Zikos, S., Karatza, H.D.: Performance and energy aware cluster-level scheduling of compute-intensive jobs with unknown service times. Simulation Modelling Practice and Theory, 239–250 (2011)Google Scholar
  16. 16.
    AMD Opteron Processor-Based Server Benchmarks (2010),
  17. 17.
    SPEC’s Benchmarks and Published Results (2010),
  18. 18.
    Feng, W., Cameron, K.: Power Measurement of High-End Clusters, The Green500 List, Version 0.1 (November 12, 2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xuan T. Tran
    • 1
    • 2
  • Binh T. Vu
    • 1
  1. 1.Department of Networked Systems and ServicesBudapest University of Technology and EconomicsBudapestHungary
  2. 2.Faculty of Electronics and Communication technologyThai Nguyen University of Information and Communication technologyThai NguyenVietnam

Personalised recommendations