RETRACTED CHAPTER: A Negotiation Method for Task Allocation with Time Constraints in Open Grid Environments

  • Yan Kong
  • Minjie Zhang
  • Dayong Ye
  • Xudong Luo
Part of the Studies in Computational Intelligence book series (SCI, volume 596)


This paper addresses the task allocation problem in open, dynamic grid or service-oriented environments. In such environments, both grid/service providers and consumers can be modeled as intelligent agents. These agents can leave off and enter into an open environment freely at any time. Task allocation under time constraints becomes a critical issue in such environments since it is difficult to apply a central controller during the allocation process due to the openness of environments as well as decentralized natures of agents. This paper proposes a negotiation-based method for task allocation under time constraints in an open, dynamic grid environment, where both consumer and provider agents can enter into or leave off the environment freely. In this method, there is no central controller, and agents negotiate with each other for task allocation based only on local views. The experimental results show that the proposed method can outperform state-of-art methods in terms of success rate of task allocation and total profit obtained from the allocated tasks by agents under different time constraints.


Self-interested Agent Negotiation Task allocation 


  1. 1.
    An, B., Gatti, N., Lesser, V.: Bilateral bargaining with one-sided two-type uncertainty. In: Proceedings of the International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 2, pp. 403–410 (2009)Google Scholar
  2. 2.
    An, B., Lesser, V., Irwin, D., Zink, M.: Automated negotiation with decommitment for dynamic resource allocation in cloud computing. In: Proceedings of AAMAS, pp. 981–988 (2010)Google Scholar
  3. 3.
    AuYoung, A., Chun, B., Snoeren, A., Vahdat, A.: Resource allocation in federated distributed computing infrastructures. In: Proceedings of the 1st Workshop on Operating System and Architectural Support for the On-demand IT InfraStructure, vol. 9 (2004)Google Scholar
  4. 4.
    Buyya, R., Abramson, D., Venugopal, S.: The grid economy. Proc. IEEE 93(3), 698–714 (2005)CrossRefGoogle Scholar
  5. 5.
    Cramton, P., Shoham, Y., Steinberg, R.: Combinatorial Auctions. MIT Press, Cambridge (2006)zbMATHGoogle Scholar
  6. 6.
    Dash, R.K., Vytelingum, P., Rogers, A., David, E., Jennings, N.R.: Market-based task allocation mechanisms for limited-capacity suppliers. IEEE Trans. Syst., Man Cybern., Part A: Syst. Hum. 37(3), 391–405 (2007)CrossRefGoogle Scholar
  7. 7.
    de Weerdt, M., Zhang, Y., Klos, T.: Distributed task allocation in social networks. In: Proceedings of AAMAS, p. 76 (2007)Google Scholar
  8. 8.
    Dias, M.B., Zlot, R., Kalra, N., Stentz, A.: Market-based multirobot coordination: a survey and analysis. Proc. IEEE 94(7), 1257–1270 (2006)CrossRefGoogle Scholar
  9. 9.
    Dooley, K.: Designing Large-Scale LANs. O’Reilly Media Inc., Sebastopol (2001)zbMATHGoogle Scholar
  10. 10.
    Fatima, S.S., Wooldridge, M.: Adaptive task and resource allocation in multi-agent systems. In: Proceedings of the 5th International Conference on Autonomous Agents, pp. 537–544 (2001)Google Scholar
  11. 11.
    Fu, Y., Chase, J., Chun, B., Schwab, S., Vahdat, A.: Sharp: an architecture for secure resource peering. In: ACM SIGOPS Operating Systems Review, vol. 37, pp. 133–148. ACM (2003)CrossRefGoogle Scholar
  12. 12.
    Gatti, N., Giunta, D., Marino, S.: Alternating-offers bargaining with one-sided uncertain deadlines: an efficient algorithm. Artif. Intell. 172(8), 1119–1157 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Irwin, D., Chase, J., Grit, L., Yumerefendi, A., Becker, D., Yocum, K.G.: Sharing networked resources with brokered leases. In: Proceedings of the USENIX Technical Conference, pp. 199–212 (2006)Google Scholar
  14. 14.
    Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Wooldridge, M.J., Sierra, C.: Automated negotiation: prospects, methods and challenges. Group Decis. Negot. 10(2), 199–215 (2001)CrossRefGoogle Scholar
  15. 15.
    Klos, T., Nooteboom, B.: Adaptive learning in evolving task allocation networks. In: Proceedings of AAMAS, pp. 465–472 (2009)Google Scholar
  16. 16.
    Lai, K., Rasmusson, L., Adar, E., Zhang, L., Huberman, B.A.: Tycoon: an implementation of a distributed, market-based resource allocation system. Multiagent Grid Syst. 1(3), 169–182 (2005)CrossRefGoogle Scholar
  17. 17.
    Macarthur, K.S., Stranders, R., Ramchurn, S.D., Jennings, N.R.: A distributed anytime algorithm for dynamic task allocation in multi-agent systems. In: Proceedings of AAAI, pp. 356–362 (2011)Google Scholar
  18. 18.
    Regev, O., Nisan, N.: The popcorn market. Online markets for computational resources. Decis. Support Syst. 28(1), 177–189 (2000)CrossRefGoogle Scholar
  19. 19.
    Rubinstein, A.: Perfect equilibrium in a bargaining model. Econom.: J. Econom. Soc. 50(1), 97–109 (1982)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Theocharopoulou, C., Partsakoulakis, I., Vouros, G.A., Stergiou, K.: Overlay networks for task allocation and coordination in dynamic large-scale networks of cooperative agents. In: Proceedings of AAMAS, p. 55 (2007)Google Scholar
  21. 21.
    Zheng, X., Koenig, S.: Reaction functions for task allocation to cooperative agents. In: Proceedings of AAMAS, pp. 559–566 (2008)Google Scholar

Copyright information

© Springer Japan 2015

Authors and Affiliations

  1. 1.School of Computer Science and Software EngineeringUniversity of WollongongWollongongAustralia
  2. 2.Institute of Logic and CognitionSun Yat-sen UniversityGuangzhouChina

Personalised recommendations