Combinatorial auctions for exchanging resources over a grid network


In grid systems, users compete for different types of resources such that they may execute their applications. Traditional grid systems are formed of organisations that join together for the purpose of collaborative projects. Resources of each of the participating organisation are pooled such that members of individual organisations may access the shared infrastructure. In general, each participant is both a provider and a consumer of resources. Whilst such systems address large organisations, in this paper we address democratic grid systems to satisfy needs of small organisations and even individuals, where on-demand grids may be formed by drawing idling resources available on the Internet. Whilst traditional grid systems resort to allocations that satisfy system specific objectives such as maximization of the resource utilisation, market mechanisms try to obtain allocations that are efficient economically. Economic mechanisms permit to achieve equilibrium between supply and demand and furthermore provide incentives for providers. Combinatorial auction has been argued as an effective mechanism to address the problem of resource allocation within grid systems. Auctions within which multiple types of resources in varying quantities may be traded eliminate the exposure problem by addressing co-allocation. In this paper, we describe a combinatorial exchange where multiple providers and multiple consumers may participate. We describe the winner determination problem that incorporates the time dimension, i.e. resource bundles may be requested for different time ranges, and describe a set of heuristics that have been designed to be fast. We show that these achieve a high level of efficiency as compared to exact solutions. The second part focusses on the pricing problem. The objective is to compute prices that represent the state of the market and bring trustworthy feedback to participants. Drawing on the approach taken by Kwasnica et al. (Manage Sci 51(3):419–434, 2005), we propose a pricing model that computes per-item pricing. Per-item pricing allows users to deduce the price of bundles that they require by linear summation. Furthermore, we propose a model that computes prices as a function of time, thus permitting users, in particular consumers to adjust their demand trading off price and time of execution.

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  1. 1.

    Bapna R, Das S, Garfinkel R, Stallaert J (2006) A market design for grid computing. INFORMS J Comput 20:100–111

    Article  Google Scholar 

  2. 2.

    Chun BN, Ng C, Albrecht J, Parkes DC, Vahdat A (2004) A multiattribute combinatorial exchange for trading grid resources. Technical report

  3. 3.

    De Vries S, Vohra R (2003) Combinatorial auctions: a survey. J Comput 15(3):284–309

    MathSciNet  Google Scholar 

  4. 4.

    Foster I (2002) The Grid: a new infrastructure for 21st century science. Phys Today 55:42–47

    Article  Google Scholar 

  5. 5.

    Mosßmann M, Stößer J, Ouorou A, Gourdin E, Krishnaswamy R, Neumann D (2009) A combinatorial exchange for complex grid services. In: Neumann D, Baker M, Altmann J, Rana O (eds) Economic models and algorithms for distributed systems. Birkhaüser, Basel, pp 221–237

    Chapter  Google Scholar 

  6. 6.

    Kwasnica AM, Ledyard JO, Porter D, DeMartini C (2005) A new and improved design for multiobject iterative auctions. Manage Sci 51(3):419–434

    Article  Google Scholar 

  7. 7.

    MacKie-Mason JK, Wellman MP (2006) Automated markets and trading agents. In: Tesfatsion L, Judd K (eds) Handbook of computational economics, vol 2. North-Holland

  8. 8.

    Rothkopf MH (2007) Thirteen reasons why the Vickrey–Clarkes–Groves process is not practical. Oper Res 55(2): 191–197

    MATH  Article  Google Scholar 

  9. 9.

    Myerson RB, Satterthwaite MA (1983) Efficient mechanisms for bilateral trading. J Econ Theory 29:265–281

    MATH  Article  MathSciNet  Google Scholar 

  10. 10.

    Parkes DC, Kalagnanam J, Eso M (2001) Achieving budget-balance with Vicrey-based payment schemes in exchanges. In: IJCAI-01: proc. 17th intern. joint conference on artificial intelligence, pp 1161–1168

  11. 11.

    Preston McAfee RP (1992) A dominant strategy double auction. J Econ Theory 56:434–450

    MATH  Article  Google Scholar 

  12. 12.

    Sandholm T, Suri S, Gilpin A, Levine D (2001) Winner determination in combinatorial auction generalizations. In: Proceedings of the 1st international joint conference on autonomous agents and multiagent systems

  13. 13.

    Schnizler B, Neumann D, Veit D, Weinhardt C (2005) A multiattribute combinatorial exchange for trading grid resources. In: Proceedings of the research symposium on emerging electronic

  14. 14.

    Stoesser J, Neumann D (2007) GREEDEX—a scalable clearing mechanism for utility computing. In: NAEC: networking and electronic commerce research conference, Riva Del Garda, Italy

  15. 15.

    Xia M, Stallaert J, Whinston AB (2004) Solving the combinatorial double auction problem. Eur J Oper Res 164:239–251

    Article  Google Scholar 

  16. 16.

    Zirel E, Nisan N (2001) An efficient approximate allocation algorithm for combinatorial auctions. In: Proceedings of the 3rd ACM conference on electronic commerce

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This work has partially been funded by the European Union in the IST programme ‘Advance Grid Technology, Systems, and Services’ under grant FP6-2006-IST5-034567 with the title Grid4All: Dynamic Virtual Organizations for schools, families and all.

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Correspondence to Adam Ouorou.

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Denoeud-Belgacem, L., Gourdin, E., Krishnaswamy, R. et al. Combinatorial auctions for exchanging resources over a grid network. Ann. Telecommun. 65, 689–704 (2010).

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  • Combinatorial auction
  • Winner determination problem
  • Vickrey–Clarke–Groves prices