Fair Scheduling in Grid VOs with Anticipation Heuristic

  • Victor ToporkovEmail author
  • Dmitry Yemelyanov
  • Anna Toporkova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10778)


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 along with local private utilization impose specific requirements for scheduling according to different, usually contradictive, criteria. We study the problem of a fair job batch scheduling with a relatively limited resources supply. With increasing resources utilization level the available resources set and corresponding decision space are reduced. The main problem is a scarce set of job execution alternatives which eliminates scheduling optimization. In order to improve overall scheduling efficiency we propose a heuristic anticipation approach. It generates a reference, most likely infeasible, scheduling solution. A special replication procedure performs a feasible solution with a minimum distance to a reference alternative under given metrics.


Scheduling Grid Resources Utilization Heuristic Job batch Virtual organization 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 18-07-00456 and 18-07-00534), and by the Ministry on Education and Science of the Russian Federation (project no. 2.9606.2017/8.9).


  1. 1.
    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, Dordrecht (2003). 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. Human. 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. Concurr. Comput. 14(5), 1507–1542 (2002). CrossRefzbMATHGoogle Scholar
  4. 4.
    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
  5. 5.
    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). CrossRefGoogle Scholar
  6. 6.
    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).
  7. 7.
    Vasile, M., Pop, F., Tutueanu, R., Cristea, V., Kolodziej, J.: Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing. Future Gener. Comput. Syst. 51, 61–71 (2015). CrossRefGoogle Scholar
  8. 8.
    Penmatsa, S., Chronopoulos, A.T.: Cost minimization in utility computing systems. Concurr. Comput.: Pract. Exp. 16(1), 287–307 (2014). CrossRefGoogle Scholar
  9. 9.
    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).
  10. 10.
    Blanco, H., Guirado, F., Lrida, 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 (2012). Google Scholar
  11. 11.
    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). CrossRefGoogle Scholar
  12. 12.
    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).
  13. 13.
    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).
  14. 14.
    Skowron, P., Rzadca, K.: Non-monetary fair scheduling cooperative game theory approach. In: Proceeding of SPAA 2013 Proceedings of the Twenty-Fifth Annual ACM Symposium on Parallelism in Algorithms and Architectures, pp. 288–297. ACM, New York (2013).
  15. 15.
    Toporkov, V., Yemelyanov, D., Bobchenkov, A., Tselishchev, A.: Scheduling in grid based on VO stakeholders preferences and criteria. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds.) Dependability Engineering and Complex Systems. AISC, vol. 470, pp. 505–515. Springer, Cham (2016). Google Scholar
  16. 16.
    Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D.: Slot selection algorithms in distributed computing. J. Supercomput. 69(1), 53–60 (2014). CrossRefGoogle Scholar
  17. 17.
    Dimitriadou, S.K., Karatza, H.D.: Job scheduling in a distributed system using backfilling with inaccurate runtime computations. In: Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems, pp. 329–336 (2010).
  18. 18.
    Farahabady, M.H., Lee, Y.C., Zomaya, A.Y.: Pareto-optimal cloud bursting. IEEE Trans. Parallel Distrib. Syst. 25, 2670–2682 (2014). CrossRefGoogle Scholar
  19. 19.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.: Pract. Exp. 41(1), 23–50 (2011). Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Research University MPEIMoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia

Personalised recommendations