A Strategy-Proof Combinatorial Auction-Based Grid Resource Allocation System

  • Yi Liang
  • Jianping Fan
  • Dan Meng
  • Ruihua Di
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4494)


In this paper, we introduce a strongly strategy-proof combinatorial auction-based grid resource allocation system, called PheonixMarket. The key advantages of PheonixMarket are that it makes the scheduling with the time-varying job value information; guarantees the combinatorial allocation of heterogeneous resources, incents users to reveal true value information of their jobs, encourages users to contribute their redundant resources and avoids exceeding resource use by the baleful users. In the performance experiments, the economic efficiency of PheonixMarket is analyzed. We then measure the price sensitivity of PheonixMarket and make the load balance experiment based on its price ’signal’. Finally, the issue of taking the funding as a form of priority is measured in the experiments.


Grid Resource allocation Combinatorial Auction Accounting 


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  1. 1.
    Foster, I., Kesselman, C.: The grid: a Blueprint for a New Computing Infrastructure, 2nd edn. Morgan Kaufmann, San Francisco (2003)Google Scholar
  2. 2.
    Nabrzyski, J., Schof, J.M., Weglarz, J.: Grid Resource Management, State of the Art and Future Trends. Kluwer Academic Publishers, Boston (2003)Google Scholar
  3. 3.
    Buyya, R., Abramson, D., Giddy, J., Stockinger, H.: Economic models for resource allocation and scheduling in grid computing. Concurrency and Computation: Practice and Experience 14(13-15), 1507–1542 (2003)CrossRefGoogle Scholar
  4. 4.
    Grosu, D., Das, A.: Auction-based resource allocation protocols in grids. In: Proc. Of the 16th IASTED International Conference on Parallel and Distributed Computing and Systems, pp. 20–27 (November 2004) Google Scholar
  5. 5.
    Smith, R.G.: The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. IEEE Transactions on Computers 29, 1104–1113 (1980)CrossRefGoogle Scholar
  6. 6.
    Waldspurger, C.A., Hogg, T., Huberman, B.A: Spawn: A Distributed Computational Economy. IEEE Transaction on Software Engineering 18(2), 103–117 (1992)CrossRefGoogle Scholar
  7. 7.
    Pindyck, R.S.: Microeconomics. Prentice-Hall, Englewood Cliffs (2004)Google Scholar
  8. 8.
    Buyya, R., Venugopal, S.: The Gridbus toolkit for service oriented grid and utility computing: an overview and status report. In: Proc. Of the 1st IEEE International Workshop on Grid Economics and Business Models, pp. 19–66 (2004)Google Scholar
  9. 9.
    Wolski, R., Plank, J. S., Bryan, T., Brevik, J.: G-commerce: market formulations controlling resource allocation on the computational grid. In: Proc. Of the 15th IEEE International Parallel and Distributed Processing Symposium (April 2001)Google Scholar
  10. 10.
    Wolski, R., Brevilk, J.: Grid Resource Allocation and Control using Computational Economics. Concurrency: Practice and Experience 29, 1–24 (2002)Google Scholar
  11. 11.
    Regev, O., Nisan, N.: The Popcorn market – An online market for computational resources. In: Proc. Of the 1st international conference on Information and Computation economies, pp. 148–157 (1998)Google Scholar
  12. 12.
    Wen, C., Lu, D.: A double auction-based resource allocation strategy in the computational grid. Computer Research and Development 29(6), 1004–1008 (2006)Google Scholar
  13. 13.
    Assuncao, M., Buyya, R.: An evaluation of communication demand of auction Protocols in grid environments. In: Porc. Of the 3rd International Workshop on Grid Economics and Business (GECON 2006), World Scientific Press, Singapore (2006)Google Scholar
  14. 14.
    Wolski, R., Plank, J.S., Brevik, J., Bryan, T.: Analyzing market-based resource allocation strategies for the computational grid. The. international Journal of High. Performance Computing Applications 15(3), 258–281 (2001)CrossRefGoogle Scholar
  15. 15.
    Cerquides, J., Endriss, U.: Bidding Languages and winner determination for mixed multi-unit combinatorial auctions. In: Proc. Of the 20th International Joint Conference on Artificial Intelligence (to be appeared, 2007)Google Scholar
  16. 16.
    Das, A., Grosu, D.: Combinatorial Auction-based Protocols for Resource Allocation in Grids. In: Proc. Of the 19th IEEE Parallel and Distributed Processing Symposium (2005)Google Scholar
  17. 17.
    Nisan, N.: Bidding and allocaton in combinatorial auctions. In: Proc. Of the 2nd ACM Conference on Electronic Commerce, pp. 1–20 (2000)Google Scholar
  18. 18.
    Barrnote, A., Buyya, R.: GridBank: a Grid Accounting Services Architecture (GASA) for distributed systems sharing and integration. In: Proc. Of the IEEE International Parallel and Distributed Processing Symposium (April 2003)Google Scholar
  19. 19.
    Mu’alem, A., Nisan, N.: Truthful Apprximation Mechanisms for Restricted Combinatorial Auctions. In: Proc. Of the Eighteenth national conference on Artificial intelligence, pp. 379–348 (2002)Google Scholar
  20. 20.
    Lehmann, D., O’Callaghan, L.I., Shoham, Y.: Truth Revelation in Rapid, Approximately Efficient Combinatorial Auctions. Journal of the ACM 49(5), 577–602 (2002)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Zhang, Z., Meng, D., Zhan, J.: Easy and Reliable Cluster Management: The Self- management Experience of Fire Phoenix. In: Proc. Of IPDPS 2006 SMTPS workshopGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yi Liang
    • 1
  • Jianping Fan
    • 2
  • Dan Meng
    • 2
  • Ruihua Di
    • 1
  1. 1.Gird and distributed Computing Lab, Beijing University of Technology, National Research Center for Intelligent Computing System 
  2. 2.Institute of Computing Technology, Chinese Academy of Sciences 

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