An Auction and League Championship Algorithm Based Resource Allocation Mechanism for Distributed Cloud

  • Jiajia Sun
  • Xingwei Wang
  • Keqin Li
  • Chuan Wu
  • Min Huang
  • Xueyi Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8299)


In cloud computing, all kinds of idle resources can be pooled to establish a resource pool, and different kinds of resources combined as a service is provided to users through virtualization. Therefore, an effective mechanism is necessary for managing and allocating the resources. In this paper, we propose a double combinatorial auction based allocation mechanism based on the characteristics of cloud resources and inspired by the flexibility and effectiveness of microeconomic methods. The feedback evaluation based reputation system with attenuation coefficient of time and the hierarchy of users introduced is implemented to avoid malicious behavior. In order to make decisions scientifically, we propose a price decision mechanism based on a BP (back propagation) neural network, in which various factors are taken into account, so the bidding/asking prices can adapt to the changing supply-demand relation in the market. Since the winner determination is an NP hard problem, a league championship algorithm is introduced to achieve optimal allocation with the optimization goals being market surplus and total reputation. We also conduct empirical studies to demonstrate the feasibility and effectiveness of the proposed mechanism.


cloud computing double combinatorial auction reputation BP neural network league championship algorithm 


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  1. 1.
    Vasan, R.: A venture perspective on cloud computing. IEEE Computer 44(3), 60–62 (2011)CrossRefGoogle Scholar
  2. 2.
    Papazoglou, M.P., van den Heuvel, W.: Blueprinting the cloud. IEEE Internet Computing 15(6), 74–79 (2011)CrossRefGoogle Scholar
  3. 3.
    Mell, P., Grance, T.: Definition of cloud computing. Technical report, National Institute of Standard and Technology (NIST) (July 2009)Google Scholar
  4. 4.
    Tan, Z., Gurd, J.R.: Market-based grid resource allocation using a stable continuous double auction. In: IEEE/ACM International Conference on Grid Computing, pp. 283–290 (September 2007)Google Scholar
  5. 5.
    Danak, A., Mannor, S.: Efficient bidding in dynamic grid markets. IEEE Transactions on Parallel and Distributed Systems 22(9), 1483–1496 (2011)CrossRefGoogle Scholar
  6. 6.
    Tsai, C.W., Tsai, Z.: Bid-proportional auction for resource allocation in capacity-constrained clouds. In: International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 1178–1183 (March 2012)Google Scholar
  7. 7.
    Prodan, R., Wieczorek, M., Frad, M.: Double auction-based scheduling of scientific applications in distributed grid and cloud environment. Journal of Grid Computing 9(4), 531–548 (2011)CrossRefGoogle Scholar
  8. 8.
    Shang, S., Jiang, J., Wu, Y., Yang, G., Zheng, W.: A knowledge-based continuous double auction model for cloud market. In: International Conference on Semantics Knowledge and Grid (SKG), pp. 129–134 (November 2010)Google Scholar
  9. 9.
    Song, B., Hassan, M.M., Huh, E.N.: A novel cloud market infrastructure for trading service. In: International Conference on Computational Science and Its Applications (ICCSA), pp. 44–50 (June 2009)Google Scholar
  10. 10.
    Rassenti, S.J., Smith, V.L., Bulfin, R.L.: A combinatorial auction mechanism for airport time slot allocation. The Bell Journal of Economics 13(2), 402–417 (1982)CrossRefGoogle Scholar
  11. 11.
    Xia, M., Stallaert, J., Andrew, B.: Solving the combinatorial double auction problem. European Journal of Operational Research 164(1), 239–251 (2005)CrossRefzbMATHGoogle Scholar
  12. 12.
    Kashan, A.H.: League Championship Algorithm: A new algorithm for numerical function optimization. In: International Conference of Soft Computing and Pattern Recognition, pp. 43–48 (2009)Google Scholar
  13. 13.
    Deb, K.: An efficient constraint handling method for genetic algorithms. Computation Methods in Applied Mechanics and Engineering 86(2-4), 311–338 (2000)CrossRefGoogle Scholar
  14. 14.
    Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: International Joint Conference on Neural Networks, pp. 593–605 (1989)Google Scholar
  15. 15.
    Howell, F., McNab, R.: Simjava: a discrete event simulation package for Java with applications in computer systems modelling. In: First International Conference on Web-based Modelling and Simulation (1998)Google Scholar
  16. 16.
    Amazon Web Services,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jiajia Sun
    • 1
  • Xingwei Wang
    • 1
  • Keqin Li
    • 2
  • Chuan Wu
    • 3
  • Min Huang
    • 1
  • Xueyi Wang
    • 1
  1. 1.College of Information Science and EngineeringNortheastern UniversityUSA
  2. 2.Department of Computer ScienceState University of New YorkUSA
  3. 3.Department of Computer ScienceThe University of Hong KongHong Kong

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