Skip to main content
SpringerLink
Log in

A game-theoretic method of fair resource allocation for cloud computing services

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Al-Ali R, Amin K, von Laszewski G, Rana O, Walker D, Hategan M, Zaluzec N (2004) Analysis and provision of QoS for distributed grid applications. J Grid Comput 2(2):163–182

    Article  Google Scholar 

  2. Amoura AK, Bampis E, Kenyon C, Manoussakis Y (2002) Scheduling independent multiprocessor tasks. Algorithmica 32:247–261

    Article  MATH  MathSciNet  Google Scholar 

  3. An B, Douglis F, Ye F (2008) Heuristics for negotiation schedules in multi-plan optimization. In: Proc of the seventh international joint conference on autonomous agents and multi-agent systems, 2008, pp 551–558

  4. An B, Vasilakos AV, Lesser V (2009) Evolutionary stable resource pricing strategies. In: ACM SIGCOMM 2009, Barcelona, Spain, August 17–21, 2009

  5. Andelman N, Azar Y, Sorani M (2005) Truthful approximation mechanisms for scheduling selfish related machines. In: STOC 2005, pp 69–82

  6. Borst S, Boxma O, Groote JF, Mauw S (2003) Task allocation in a multi-server system. J Sched 6:423–436

    Article  MATH  MathSciNet  Google Scholar 

  7. Boyera WF, Hura GS (2005) Non-evolutionary algorithm for scheduling dependent tasks in distributed heterogeneous computing environments. J Parallel Distrib Comput 65:1035–1046

    Article  Google Scholar 

  8. Briest P, Krysta P, Vöcking B (2205) Approximation techniques for utilitarian mechanism design. In: STOC 2005, pp 39–48

  9. Buyya R, Abramson D, Giddy J, Stockinger H (2002) Economic models for resource management and scheduling in grid computing. Special issue on grid computing environments. J Concurr Comput: Pract Exp (CCPE) 14:13–15

    Google Scholar 

  10. Christodoulou G, Koutsoupias E, Kovacs A (2007) Mechanism design for fractional scheduling on unrelated machines. In: ICALP 2007

  11. Christodoulou G, Koutsoupias E, Vidali A (2007) A lower bound for scheduling mechanisms. In: SODA 2007, pp 1163–1169

  12. Collins DE, George AD (2001) Parallel and sequential job scheduling in heterogeneous clusters: a simulation study using software in the loop. Simulation 77:169–184

    Article  Google Scholar 

  13. Dobzinski S, Nisan N, Schapira M (2005) Approximation algorithms for combinatorial auctions with complement-free bidders. In: STOC 2005, pp 610–618

  14. Dogan A, Özgüner F (2002) Scheduling independent tasks with QoS requirements in grid computing with time-varying resource prices. In: CCGRID 2002, pp 58–69

  15. Doulamis N, Doulamis A, Litke A, Panagakis A, Varvarigou T, Varvarigos E (2007) Adjusted fair scheduling and non-linear workload prediction for QoS guarantees in grid computing. Comput Commun 30:499–515

    Article  Google Scholar 

  16. Hochbaum DS (1996) Approximation algorithms for NP-hard problems. PWS Publishing, Boston

    Google Scholar 

  17. Iverson MA, Ozguner F, Potter L (1999) Statistical prediction of task execution times through analytic benchmarking for scheduling in a heterogeneous environment. IEEE Trans Comput 48(12):1374–1379

    Article  Google Scholar 

  18. Karatza HD, Hilzer RC (2003) Parallel job scheduling in homogeneous distributed systems. Simulation 79(5–6):287–298

    Google Scholar 

  19. Koole G, Righter R (2008) Resource allocation in grid computing. J Sched 11:163–173

    Article  MATH  Google Scholar 

  20. Korkhov VV, Krzhizhanovskaya VV, Sloot PMA (2008) A grid-based virtual reactor: parallel performance and adaptive load balancing. J Parallel Distrib Comput 68:596–608

    Article  Google Scholar 

  21. Korkhova VV, Moscicki JT, Krzhizhanovskaya VV (2009) Dynamic workload balancing of parallel applications with user-level scheduling on the grid. Future Gener Comput Syst 25:28–34

    Article  Google Scholar 

  22. Lavi R, Swamy C (2007) Truthful mechanism design for multi-dimensional scheduling via cycle-monotonicity. In: EC 2007

  23. Lenstra JK, Shmoys DB, Tardos E (1990) Approximation algorithms for scheduling unrelated parallel machines. Math Program 46(1):259–271

    Article  MATH  MathSciNet  Google Scholar 

  24. Li K (2004) Experimental performance evaluation of job scheduling and processor allocation algorithms for grid computing on metacomputers. In: IPDPS 2004, pp 170–177

  25. Mualem A, Schapira M (2007) Setting lower bounds on truthfulness. In: SODA 2007, pp 1143–1152

  26. Nisan N, Ronen A (1999) Algorithmic mechanism design (extended abstract). In: STOC 1999, pp 129–140

  27. Nisan N, Ronen A (2001) Algorithmic mechanism design. Games Econ Behav 35:166–196

    Article  MATH  MathSciNet  Google Scholar 

  28. Ranganathan K, Ripeanu M, Sarin A, Foster I (2004) Incentive mechanisms for large collaborative resource sharing. In: CCGrid 2004, pp 1-8

  29. Schoneveld A, de Ronde JF, Sloot PMA (1997) On the complexity of task allocation. Complexity 3:52–60

    Article  MathSciNet  Google Scholar 

  30. Schopf JM (2003) Ten actions when grid scheduling. In: Nabrzyski J, Schopf JM, Weglarz J (eds) Grid resource management: state of the art and future trends. Kluwer, Dordrecht (Chap 2)

    Google Scholar 

  31. Sonmez OO, Gursoy A (2007) A novel economic-based scheduling heuristic for computational grids. Int J High Perform Comput Appl 21(1):21–29

    Article  Google Scholar 

  32. Wolski R, Plank JS, Brevik J, Bryan T (2001) G-commerce: Market formulations controlling resource allocation on the computational grid. In: Proceeding of IPDPS 2001, p 46

Download references

Author information

Authors and Affiliations

  1. Zhejiang Gongshang University, Hangzhou, Zhejiang, People’s Republic of China

    Guiyi Wei

  2. National Technical University of Athens, Athens, Greece

    Athanasios V. Vasilakos

  3. Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

    Yao Zheng

  4. Department of Computer Science, Georgia State University, Atlanta, USA

    Naixue Xiong

Authors
  1. Guiyi Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Athanasios V. Vasilakos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yao Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Naixue Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Athanasios V. Vasilakos.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wei, G., Vasilakos, A.V., Zheng, Y. et al. A game-theoretic method of fair resource allocation for cloud computing services. J Supercomput 54, 252–269 (2010). https://doi.org/10.1007/s11227-009-0318-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-009-0318-1

Keywords

Search

Navigation