Heuristic of Anticipation for Fair Scheduling and Resource Allocation in Grid VOs

  • Victor Toporkov
  • Anna Toporkova
  • Dmitry Yemelyanov
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 737)

Abstract

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. In order to improve overall scheduling efficiency, we propose an anticipation scheduling heuristic. It includes a target (anticipated) pattern solution definition and a special replication procedure for efficient and feasible resources allocation. A proposed anticipation algorithm is compared against conservative backfilling variations using such criteria as average jobs response time (start and finish times) as well as users and VO economic criteria (execution time and cost).

Keywords

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

Notes

Acknowledgements

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. 2.9606.2017/8.9).

References

  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). doi: 10.1109/CISIS.2010.65
  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. Hum. Comput. 6(6), 733–740 (2015). doi: 10.1007/s12652-015-0274-y CrossRefGoogle Scholar
  3. 3.
    Buyya, R., Abramson, D., Giddy, J.: Economic models for resource management and scheduling in grid computing. J. Concurrency Comput. 14(5), 1507–1542 (2002). doi: 10.1002/cpe.690 CrossRefMATHGoogle Scholar
  4. 4.
    Kurowski, K., Nabrzyski, J., Oleksiak, A. and Weglarz, J.: Multicriteria aspects of grid resource management. In: Nabrzyski, J., Schopf, J.M. and Weglarz, J. (eds.) Grid Resource Management. State of the Art and Future Trends, pp. 271–293 (2003). doi: 10.1007/978-1-4615-0509-9_18
  5. 5.
    Rodero, I., Villegas, D., Bobro, N., Liu, Y., Fong, L., Sadjadi, S.M.: Enabling interoperability among grid meta-schedulers. J. Grid Comput. 11(2), 311–336 (2013). doi: 10.1007/s10723-013-9252-9 CrossRefGoogle Scholar
  6. 6.
    Ernemann, C., Hamscher, V., Yahyapour, R.: Economic scheduling in grid computing. In: Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing, vol. 2537, pp. 128–152. Springer, Berlin, Heidelberg (2002). doi: 10.1007/3-540-36180-4_8
  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 (2007). doi: 10.1109/ccgrid.2007.52
  8. 8.
    Penmatsa, S., Chronopoulos, A.T.: Cost minimization in utility computing systems. Concurrency Comput.: Pract. Experience 16(1), 287–307 (2014). doi: 10.1002/cpe.2984 CrossRefGoogle Scholar
  9. 9.
    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). doi: 10.1016/j.future.2014.11.019 CrossRefGoogle Scholar
  10. 10.
    Mutz, A., Wolski, R. and Brevik, J.: Eliciting honest value information in a batch-queue environment. In: 8th IEEE/ACM International Conference on Grid Computing, pp. 291–297, IEEE Computer Society (2007). doi: 10.1109/grid.2007.4354145
  11. 11.
    Blanco, H., Guirado, F., Lrida, J.L., Albornoz, V.M.: MIP model scheduling for multi-clusters. Proc. Euro-Par 2012, 196–206 (2012). doi: 10.1007/978-3-642-36949-0_22 Google 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: 15th International Workshop JSSPP 2010, vol. 6253, pp. 16–34 (2010). doi: 10.1007/978-3-642-16505-4_2
  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 07), pp. 36–36 (2007). doi: 10.1109/ispdc.2007.16
  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 (2007). doi: 10.1109/grid.2007.4354115
  15. 15.
    Toporkov, V., Yemelyanov, D., Bobchenkov, A., Tselishchev, A.: Scheduling in Grid Based on VO Stakeholders Preferences and Criteria. Advances in Intelligent Systems and Computing, vol. 470, pp. 505–515. Springer International Publishing Switzerland (2016). doi: 10.1007/978-3-319-39639-2_44
  16. 16.
    Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D.: Slot selection algorithms in distributed computing. J. Supercomput. 69(1), 53–60 (2014). doi: 10.1007/s11227-014-1210-1 CrossRefGoogle Scholar
  17. 17.
    Toporkov, V., Tselishchev, A., Yemelyanov, D., Bobchenkov, A.: Composite scheduling strategies in distributed computing with non-dedicated resources. Proc. Comput. Sci. 9, 176–185 (2012). doi: 10.1016/j.procs.2012.04.019
  18. 18.
    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. J. Softw.: Pract. Experience 41(1), 23–50 (2011). doi: 10.1002/spe.995

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

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

Personalised recommendations