Advertisement

Heuristic Anticipation Scheduling in Grid with Non-dedicated Resources

  • Victor V. Toporkov
  • Dmitry M. YemelyanovEmail author
  • Petr A. Potekhin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 753)

Abstract

A heuristic user job-flow scheduling approach to grid virtual organizations with non-dedicated resources is discussed in this article. Users’ and resource providers’ preferences, virtual organization’s internal policies, resources geographical distribution along with local private utilization impose specific requirements for efficient scheduling according to different, usually contradictive, criteria. The available resources set and the corresponding decision space decrease as resources utilization increases. This introduces further complications into the task of efficient scheduling. We propose a heuristic anticipation scheduling approach to improve the overall scheduling efficiency. Initially, it generates a near optimal but infeasible scheduling solution which is then used as a reference for efficient allocation of resources.

Keywords

Scheduling Grid Resources Utilization Heuristic Job batch Virtual organization Anticipation 

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. Humanized 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. Concurr. Comput. 14(5), 1507–1542 (2002). doi: 10.1002/cpe.690 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, vol. 64, pp. 271–293. Springer, Boston (2003). doi: 10.1007/978-1-4615-0509-9_18 CrossRefGoogle Scholar
  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: 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 (2007). doi: 10.1109/ccgrid.2007.52
  8. 8.
    Penmatsa, S., Chronopoulos, A.T.: Cost minimization in utility computing systems. Concurr. Comput. Pract. Exp. 16(1), 287–307 (2014). doi: 10.1002/cpe.2984. WileyCrossRefGoogle 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., 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., 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: 15th International Workshop JSSP 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 2007), p. 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.
    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 (SPAA 2013), pp. 288–297 (2013). doi: 10.1145/2486159.2486169
  16. 16.
    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). doi: 10.1007/978-3-319-39639-2_44 Google Scholar
  17. 17.
    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
  18. 18.
    Farahabady, M.H., Lee, Y.C., Zomaya, A.Y.: Pareto-optimal cloud bursting. IEEE Trans. Parallel Distrib. Syst. 25, 2670–2682 (2014). doi: 10.1109/tpds.2013.218 CrossRefGoogle Scholar
  19. 19.
    Cafaro, M., Mirto, M., Aloisio, G.: Preference-based matchmaking of grid resources with CP-Nets. J. Grid Comput. 11(2), 211–237 (2013). doi: 10.1007/s10723-012-9235-2 CrossRefGoogle Scholar
  20. 20.
    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). doi: 10.1016/j.procs.2012.04.019 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Research University “Moscow Power Engineering Institute”MoscowRussia

Personalised recommendations