Skip to main content
Log in

Dynamic scheduling model of computing resource based on MAS cooperation mechanism

  • Published:
Science in China Series F: Information Sciences Aims and scope Submit manuscript

Abstract

Allocation of grid resources aims at improving resource utility and grid application performance. Currently, the algorithms proposed for this purpose do not fit well the autonomic, dynamic, distributive and heterogeneous features of the grid environment. According to MAS (multi-agent system) cooperation mechanism and market bidding game rules, a model of allocating allocation of grid resources based on market economy is introduced to reveal the relationship between supply and demand. This model can make good use of the studying and negotiating ability of consumers’ agent and takes full consideration of the consumer’s behavior, thus rendering the application and allocation of resource of the consumers rational and valid. In the meantime, the utility function of consumer is given; the existence and the uniqueness of Nash equilibrium point in the resource allocation game and the Nash equilibrium solution are discussed. A dynamic game algorithm of allocating grid resources is designed. Experimental results demonstrate that this algorithm diminishes effectively the unnecessary latency, improves significantly the smoothness of response time, the ratio of throughput and resource utility, thus rendering the supply and demand of the whole grid resource reasonable and the overall grid load balanceable.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Baruah S K, Cohen N K. Plaxton in resource allocation. Algorithmica, 1996, 15(6): 600–625

    Article  MATH  MathSciNet  Google Scholar 

  2. Wolski R, Plank J S, Brevik J. Analyzing market-based resource allocation strategies for the computational grid. Int J High Perform Comput Appl, 2001, 15(3): 258–281

    Article  Google Scholar 

  3. Subramoniam K, Maheswaran M, Toulouse M. Towards a misty economic model for resource allocation I grid computing system. In: The 2002 IEEE Canadian Conf on Electrical & Computer Engineering, Manitoba, 2002. 278–290

  4. Buyya R. Economic-based distributed resource management and allocation for grid computing. Dissertation for the Doctoral Degree. Melbourne, Australia: Monash University, 2002. 42–47

    Google Scholar 

  5. Cheng J Q, Wellman M P. The WALRAS algorithm: convergent distributed implementation of general equilibrant outcomes. Comput Econ, 1998, 12(1): 1–24

    Article  MATH  Google Scholar 

  6. Ygge F. Market-oriented programming and its application ability load management. Dissertation for the Doctoral Degree. Lund: Sweeten Lund University, 1998. 86–105

    Google Scholar 

  7. Weng C L, Lu X D. A double auction method for resource allocation on computational grids (in Chinese). Chin J Comput, 2006, 43(6): 1004–1009

    Google Scholar 

  8. Buyya R, Abramson D, Venugopal S. The grid economy. Proc IEEE, Special issue on grid computing, 2005, 93(3): 698–714

    Google Scholar 

  9. Buyya R, Vazhkudai S. Compute ability market: Towards a market-oriented grid. In: CCGRID. San Francisco: IEEE Computer Society Press, 2001. 574–581

    Google Scholar 

  10. Buyya R, Abramson D, Giddy J. A case for economy grid architecture for service-oriented grid computing. In: Proc of the 10th IEEE Int Heterogeneous Computing Workshop. Washington: IEEE Computer Society, 2001. 776–790

    Google Scholar 

  11. Buyya R, Murshed M. GridSim: A toolkit for modeling and simulation of grid resource management and allocation. J Concurr Comput Pract Exper, 2002, 14(13–15): 1175–1220

    Article  MATH  Google Scholar 

  12. Abramson D, Buyya R, Giddy J. A computational economy for grid computing and its implementation in the nimrod-G resource broker. Future Gener Comput Syst, 2002, 18(8): 1061–1074

    Article  MATH  Google Scholar 

  13. Subramoniam K, Maheswaran M, Toulouse M. Towards a microeconomic model for resource allocation in grid computing system. In: The 2002 IEEE Canadian Conf on Electrical & Computer Engineering, ManitOBA, Canada, 2002. 373–391

  14. Cao H Q, Xiao N, Lu X C, et al. A market-based approach to allocate resources for computational grids (in Chinese). J Comput Res Develop, 2002, 39(8): 913–916

    Google Scholar 

  15. Wolski R, Plank J S, Brevik J, et al. Analyzing market-based resource allocation strategies for the computational grid. Int J High Perform Comput Appl, 2001, 15(3): 258–281

    Article  Google Scholar 

  16. Chun B N, Ng J, Parkes D C. Computational resource exchanges for distributed resource allocation. Technical Report, Harvard University, 2004

  17. Feldman M, Lai K, Zhang L. A price-anticipating resource allocation mechanism for distributed shared clusters. In: Riedl J, ed. Proc of the 6th ACM Conf on Electronic Commerce. New York: ACM Press, 2005. 127–136

    Chapter  Google Scholar 

  18. Nash J F. Non-cooperative games. Ann Math, 1951, 54(2): 286–295

    Article  MathSciNet  Google Scholar 

  19. Kwok Y K, Song S S, Hwang K. Selfish grid computing: gametheoretic modeling and NAS performance results. In: Proc of the IEEE Int Symp on Cluster Computing and the Grid. Washington: IEEE Computer Society, 2005. 349–356

  20. Bredin J, Kotz D, Rus D, et al. Computational markets to regulate mobile-agent systems. Auton Agent Multi-Ag, 2003, 6(3): 235–263

    Article  Google Scholar 

  21. Maheswaran R T, Basar T. Nash equilibrium and decentralized negotiation in auctioning divisible resources. Group Decision Negotiat, 2003, 12(5): 361–395

    Article  Google Scholar 

  22. Jin Y, Kesidis G. Nash equilibrium of a generic networking game with applications to circuit-switched networks. In: Proc of IEEE INFOCOM 2003. San Francisco: IEEE Computer Society Press, 2003. 2.1242–1249

    Google Scholar 

  23. Waidspurger C, Hogg T. Spawn: a distributed computational economy. IEEE Trans Softw Eng, 1992, 18(2): 18–32

    Google Scholar 

  24. Bredin J, Kotz D, Rus D. Utility driven mobile agent allocation: [Tech Rep. CS-TR98-331]. Dartmouth college, Hanover, NH, 1998. 191–206

    Google Scholar 

  25. Archer A, Tardos E. Truthful mechanisms for one- parameter agents. In: Proceedings of the 42nd IEEE Symposium on Foundations of Computer Science. Lasvegas: IEEE Computer Society Press, 2001. 482–491

    Google Scholar 

  26. Jiang W J, Wang P. Research on distributed solution and correspond consequence of complex system based on MAS. J Comput Res Develop (in Chinese), 2006, 43(9): 1615–1623

    Article  MathSciNet  Google Scholar 

  27. Rajkumar R, Lee C, Lehoczky J, et al. Practical solutions for QoS-based resource allocation problems. In: Proc of the 19th IEEE Real-Time Systems Symopsium. Masrid: IEEE Computer Society Press, 1998. 482–491

  28. Contreras J, Candiles O. A cobweb bidding model for competitive electricity markets. IEEE Trans Abil Syst, 2002, 17(1): 51–64

    Google Scholar 

  29. Cao X R, Shen H X, Milito R, et al. Internet pricing with a game theoretical approach: concepts and examples. IEEE/ACM Trans Netw, 2002, 10(2): 208–216

    Article  Google Scholar 

  30. Roth A E. Game theory as a tool for market design. In: Fervent P, Garcia J, StefTijs, eds. Game Practice: Contributions from Applied Game Theory, Kluwer, 2000. 7–18

  31. Zhang W Z, Fang B X, Hu M Z. Multisite co-allocation scheduling algorithms for parallel jobs in computing grid environments. Sci China Ser F-Inf Sci, 2006, 49(6): 906–926

    Article  Google Scholar 

  32. Xu K, Wang Y X, Wu C. Formal verification technique for grid ser-vice chain model and its application. Sci China Ser F-Inf Sci, 2007, 49(1): 1–20

    Article  MathSciNet  Google Scholar 

  33. Gao Y, Huang J Z, Rong H. Adaptive Grid job allocation with genetic algorithm. Future Genre Comp Syst, 2005, 21: 151–161

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to LianMei Zhang.

Additional information

Supported by the Natural Science Foundation of Hunan Province (Grant No. 06JJ2033), and the Society Science Foundation of Hunan Province (Grant No. 07YBB239)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jiang, W., Zhang, L. & Wang, P. Dynamic scheduling model of computing resource based on MAS cooperation mechanism. Sci. China Ser. F-Inf. Sci. 52, 1302–1320 (2009). https://doi.org/10.1007/s11432-009-0151-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-009-0151-4

Keywords

Navigation