Heuristic-Based Job Flow Allocation in Distributed Computing

  • Victor ToporkovEmail author
  • Anna Toporkova
  • Alexey Tselishchev
  • Dmitry Yemelyanov
  • Petr Potekhin
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 616)


In this paper, we propose a meta-data based approach for a deliberate job flow distribution in computing environments, such as utility Grids. Under conditions of a heterogeneous job flow composition and a variety of resource domains, we examine how different job and resource characteristics affect the efficiency of the scheduling process. Based on the most significant job flow and resource domain characteristics a heuristic distribution quality indicator is introduced. Additional simulation study is performed to verify the indicator in different distribution strategies and to compare them with a random job flow allocation.


Virtual Organization Schedule Interval Resource Request Schedule Efficiency Schedule Cycle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by the Council on Grants of the President of the Russian Federation for State Support of Young Scientists and Leading Scientific Schools (grants YPhD-4148.2015.9 and SS-362.2014.9), RFBR (grants 15-07-02259 and 15-07-03401), the Ministry on Education and Science of the Russian Federation, task no. 2014/123 (project no. 2268), and by the Russian Science Foundation (project no. 15-11-10010).


  1. 1.
  2. 2.
    Berman, F., Wolski, R., Casanova, H.: Adaptive computing on the Grid using AppLeS. Trans. Parallel Distrib. Syst. 14(4), 369–382 (2003)CrossRefGoogle Scholar
  3. 3.
    Buyya, R., Abramson, D., Giddy, J.: Economic models for resource management and scheduling in Grid computing. J. Concurr. Comput. 14(5), 1507–1542 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Cafaro, M., Mirto, M., Aloisio, G.: Preference-based matchmaking of Grid resources with CP-Nets. J. Grid Comput. 11(2), 211–237 (2013)CrossRefGoogle Scholar
  5. 5.
    Cirne, W., Brasileiro, F., Costa, L., Paranhos, D., Santos-neto, E., Andrade, N., Grande, C.: Scheduling in bag-of-task grids: the PAUA case. In: Proceedings of the 16th Symposium on Computer Architecture and High Performance Computing, pp. 124–131. IEEE Computer Society Press (2004)Google Scholar
  6. 6.
    Dail, H., Sievert, O., Berman, F., Casanova, H., Yarkhan, A., Vadhiyar S., Dongarra, J., Liu, C., Yang, L., Angulo, D., Foster, I.: Scheduling in the grid application development software project. In: Nabrzyski, J., Schopf, J.M., Weglarz, J. (eds.) Grid Resource Management. State of the Art and Future Trends, pp. 73–98. Kluwer Academic Publisher (2003)Google Scholar
  7. 7.
    Ernemann, C., Hamscher, V., Yahyapour, R.: Economic scheduling in Grid computing. In: Feitelson, D., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP, vol. 18, pp. 128–152. Springer, Heidelberg (2002)Google Scholar
  8. 8.
    Garg, S.K., Konugurthi, P., Buyya, R.: A linear programming-driven genetic algorithm for meta-scheduling on utility Grids. J. Par. Emergent Distr. Syst. 26, 493–517 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kannan, S., Roberts, M., Mayes, P.: Workload management with LoadLeveler (2001)Google Scholar
  10. 10.
    Kurowski, K., Oleksiak, A., Nabrzyski, J.: Multi-criteria grid resource management using performance prediction techniques. In: Gorlatch, S., Danelutto, M. (eds.) Integrated Research in GRID Computing, pp. 215–225. Springer, Berlin (2007)Google Scholar
  11. 11.
    Mutz, A., Wolski, R., Brevik, J.: Eliciting honest value information in a batch-queue environment. In: 8th IEEE/ACM International Conference on Grid Computing, pp. 291–297, New York. ACM (2007)Google Scholar
  12. 12.
    Soner, S., Ozturan, C.: Integer programming based heterogeneous CPU-GPU cluster scheduler for SLURM resource manager. In: 14th IEEE International Conference on High Performance Computing and Communication and 9th IEEE International Conference on Embedded Software and Systems, pp. 418–424, Liverpool. IEEE (2012)Google Scholar
  13. 13.
    Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D.: Slot selection algorithms in distributed computing. J. Supercomput. 69(1), 53–60 (2014)CrossRefGoogle Scholar
  14. 14.
    Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D., Potekhin, P.: Preference-based fair resource sharing and scheduling optimization in Grid VOs. Procedia Comput. Sci. 29, 831–843 (2014)CrossRefGoogle Scholar
  15. 15.
    Toporkov, V., Tselishchev, A., Yemelyanov, D., Bobchenkov, A.: Composite scheduling strategies in distributed computing with non-dedicated resources. Procedia Comput. Sci. 9, 176–185 (2012)CrossRefGoogle Scholar
  16. 16.
    Toporkov, V.V., Yemelyanov, D.M.: Economic model of scheduling and fair resource sharing in distributed computations. Program. Comput. Softw. 40(1), 35–42 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Tsafrir, D., Etsion, Y., Feitelson, D.: Backfilling using system-generated predictions rather than user runtime estimates. In: Transactions on Parallel and Distributed Systems, pp. 789–803. IEEE (2007)Google Scholar
  18. 18.
    Voevodin, V.: The solution of large problems in distributed computational media. Autom. Remote Control 68(5), 773–786 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Zhou, Z., Lan, Z., Tang, W., Desai, N.: Reducing energy costs for IBM Blue Gene/P via power-aware job scheduling. In: Seventeenth Workshop on Job Scheduling Strategies for Parallel Processing, pp. 96–115, Massachusetts (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Victor Toporkov
    • 1
    Email author
  • Anna Toporkova
    • 2
  • Alexey Tselishchev
    • 3
  • Dmitry Yemelyanov
    • 1
  • Petr Potekhin
    • 1
  1. 1.National Research University “MPEI”MoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia
  3. 3.European Organization for Nuclear Research (CERN)Geneva 23Switzerland

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