Cyclic Anticipation Scheduling in Grid VOs with Stakeholders Preferences

  • Victor ToporkovEmail author
  • Dmitry Yemelyanov
  • Anna Toporkova
  • Petr Potekhin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10421)


In this work, a job-flow scheduling approach for Grid virtual organizations (VOs) is proposed and studied. Users’ and resource providers’ preferences, VOs internal policies, resources geographical distribution along with local private utilization impose specific requirements for efficient scheduling according to different, usually contradictive, criteria. With increasing resources utilization level the available resources set and corresponding decision space are reduced. This further complicates the problem of efficient scheduling. In order to improve overall scheduling efficiency, we propose an anticipation scheduling approach based on a cyclic scheduling scheme. It generates a near optimal but infeasible scheduling solution and includes a special replication procedure for efficient and feasible resources allocation. Anticipation scheduling is compared with the general cycle scheduling scheme and conservative backfilling using such criteria as average jobs’ response time (start and finish times) as well as users’ and VO economic criteria (execution time and cost).


Scheduling Grid Resources Utilization Heuristic Job batch Virtual organization Cycle scheduling scheme Anticipation Replication 



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-2297.2017.9 and SS-6577.2016.9), RFBR (grants 15-07-02259 and 15-07-03401), and by the Ministry on Education and Science of the Russian Federation (project no. 2.9606.2017/BCh).


  1. 1.
    Dimitriadou, S.K., Karatza, H.D.: Job scheduling in a distributed system using backfilling with inaccurate runtime computations. In: Proceedings of 2010 International Conference on Complex, Intelligent and Software Intensive Systems, pp. 329–336 (2010)Google Scholar
  2. 2.
    Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D., Potekhin, P.: Heuristic strategies for preference-based scheduling in virtual organizations of utility grids. J. Ambient Intell. Humanized Comput. 6(6), 733–740 (2015)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.
    Kurowski, K., Nabrzyski, J., Oleksiak, A., Weglarz, J.: Multicriteria aspects of grid resource management. In: Nabrzyski, J., Schopf, J.M., Weglarz, J. (eds.) Grid Resource Management. State of the Art and Future Trends, pp. 271–293. Kluwer Academic Publishers, Boston (2003)Google Scholar
  5. 5.
    Rodero, I., Villegas, D., Bobroff, N., Liu, Y., Fong, L., Sadjadi, S.M.: Enabling interoperability among grid meta-schedulers. J. Grid Comput. 11(2), 311–336 (2013)CrossRefGoogle Scholar
  6. 6.
    Ernemann, C., Hamscher, V., Yahyapour, R.: Economic scheduling in grid computing. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 128–152. Springer, Heidelberg (2002). doi: 10.1007/3-540-36180-4_8 CrossRefGoogle Scholar
  7. 7.
    Rzadca, K., Trystram, D., Wierzbicki, A.: Fair game-theoretic resource management in dedicated grids. In: IEEE International Symposium on Cluster Computing and the Grid (CCGRID 2007), pp. 343–350. IEEE Computer Society, Rio De Janeiro (2007)Google Scholar
  8. 8.
    Vasile, M., Pop, F., Tutueanu, R., Cristea, V., Kolodziej, J.: Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing. J. Future Gener. Comput. Syst. 51, 61–71 (2015)CrossRefGoogle Scholar
  9. 9.
    Penmatsa, S., Chronopoulos, A.T.: Cost minimization in utility computing systems. Concurr. Comput. Pract. Exp. 16(1), 287–307 (2014). WileyCrossRefGoogle Scholar
  10. 10.
    Mutz, A., Wolski, R., Brevik, J.: Eliciting honest value information in a batch-queue environment. In: 8th IEEE/ACM International Conference on Grid Computing, New York, USA, pp. 291–297 (2007)Google Scholar
  11. 11.
    Blanco, H., Guirado, F., Lérida, J.L., Albornoz, V.M.: MIP model scheduling for multi-clusters. In: Caragiannis, I., et al. (eds.) Euro-Par 2012. LNCS, vol. 7640, pp. 196–206. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36949-0_22 CrossRefGoogle Scholar
  12. 12.
    Takefusa, A., Nakada, H., Kudoh, T., Tanaka, Y.: An advance reservation-based co-allocation algorithm for distributed computers and network bandwidth on QoS-guaranteed grids. In: Frachtenberg, E., Schwiegelshohn, U. (eds.) JSSPP 2010. LNCS, vol. 6253, pp. 16–34. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-16505-4_2 CrossRefGoogle Scholar
  13. 13.
    Carroll, T., Grosu, D.: Divisible load scheduling: an approach using coalitional games. In: Proceedings of the Sixth International Symposium on Parallel and Distributed Computing, ISPDC 2007, p. 36 (2007)Google Scholar
  14. 14.
    Kim, K., Buyya, R.: Fair resource sharing in hierarchical virtual organizations for global grids. In: Proceedings of the 8th IEEE/ACM International Conference on Grid Computing, pp. 50–57. IEEE Computer Society, Austin (2007)Google Scholar
  15. 15.
    Skowron, P., Rzadca, K.: Non-monetary fair scheduling cooperative game theory approach. In: Proceedings of the Twenty-Fifth Annual ACM Symposium on Parallelism in Algorithms and Architectures, pp. 288–297. ACM, New York (2013)Google Scholar
  16. 16.
    Dalheimer, M., Pfreundt, F., Merz, P.: Agent-based grid scheduling with Calana. In: Proceedings of Parallel Processing and Applied Mathematics, 6th International Conference, pp. 741–750 (2006)Google Scholar
  17. 17.
    Jackson, D., Snell, Q., Clement, M.: Core algorithms of the Maui scheduler. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 2001. LNCS, vol. 2221, pp. 87–102. Springer, Heidelberg (2001). doi: 10.1007/3-540-45540-X_6 CrossRefGoogle Scholar
  18. 18.
    Thain, T., Livny, M.: Distributed computing in practice: the condor experience. Concurr. Comput. Pract. Exp. 17, 323–356 (2005)CrossRefGoogle Scholar
  19. 19.
    Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D., Potekhin, P.: Metascheduling and heuristic co-allocation strategies in distributed computing. Comput. Inform. 34(1), 45–76 (2015)MathSciNetGoogle Scholar
  20. 20.
    Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D.: Slot selection algorithms in distributed computing. J. Supercomput. 69(1), 53–60 (2014)CrossRefGoogle Scholar
  21. 21.
    Toporkov, V., Yemelyanov, D., Bobchenkov, A., Potekhin, P.: Fair resource allocation and metascheduling in grid with VO stakeholders preferences. In: Proceedings of the 45th International Conference on Parallel Processing Workshops, pp. 375–384. IEEE (2016)Google Scholar
  22. 22.
    Farahabady, M.H., Lee, Y.C., Zomaya, A.Y.: Pareto-optimal cloud bursting. IEEE Trans. Parallel Distrib. Syst. 25, 2670–2682 (2014)CrossRefGoogle Scholar
  23. 23.
    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

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Victor Toporkov
    • 1
    Email author
  • Dmitry Yemelyanov
    • 1
  • Anna Toporkova
    • 2
  • Petr Potekhin
    • 1
  1. 1.National Research University “MPEI”MoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia

Personalised recommendations