Skip to main content
SpringerLink
Log in

Adjustable Fuzzy Inference for Adaptive Grid Resource Negotiation

  • Chapter
Book cover Next Frontier in Agent-Based Complex Automated Negotiation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 596))

Abstract

Grids are dynamic, with the resource availability fluctuating over time, and at different rates at each instant. To obtain the resources for tasks execution, each client may negotiate with a resource allocator. If demand on resources is higher than their availability, resources can be exhausted. Therefore, a client needs to anticipate the resource availability in future, but its accurate estimation may not be possible because of the lack or inaccessibility of this information. However, through observing the information conveyed by the resource allocator in negotiation, a client can make estimates as to the speed and direction of the change in availability. In this paper, we describe an adaptive negotiation strategy that allows a client to adjust its tactics to the tendency in resource availability changes during negotiation through a fuzzy control mechanism. Results show that our negotiation strategy can allow a client to successfully obtain more resources.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Performance may include a workload of processors in the system, the available RAM, throughput, etc.

References

  1. Berman, F., Wolski, R., Casanova, H., Cirne, W., Dail, H., Faerman, M., Figueira, S., Hayes, J., Obertelli, G., Schopf, J., Shao, G., Smallen, S., Spring, N., Su, A., Zagorodnov, D.: Adaptive computing on the grid using AppLeS. IEEE Trans. Parallel Distrib. Syst. 14(4), 369–382 (2003)

    Article  Google Scholar 

  2. Borissov, N., Wirström, N.: Q-strategy: a bidding strategy for market-based allocation of grid services. In: Meersman, R., Tari, Z. (eds.) On the Move to Meaningful Internet Systems: OTM 2008. LNCS, vol. 5331, pp. 744–761. Springer, Berlin (2008)

    Chapter  Google Scholar 

  3. Chen, S., Weiss, G.: A novel strategy for efficient negotiation in complex environments. In: Timm, I., Guttmann, C. (eds.) Multiagent System Technologies. LNCS, vol. 7598, pp. 68–82. Springer, Berlin (2012)

    Chapter  Google Scholar 

  4. Downey, A.B.: Using queue time predictions for processor allocation. In: Feitelson, D.G., Rudolph, L. (eds.) Job Scheduling Strategies for Parallel Processing. LNCS, vol. 1291, pp. 35–57. Springer, Berlin (1997)

    Google Scholar 

  5. Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robot. Auton. Syst. 24(3–4), 159–182 (1998)

    Article  Google Scholar 

  6. Gwak, J., Sim, K.M.: Bayesian learning based negotiation agents for supporting negotiation with incomplete information. Lect. Notes Eng. Comput. Sci. 2188(1), 163–168 (2011)

    Google Scholar 

  7. Haberland, V., Miles, S., Luck, M.: Adaptive negotiation for resource intensive tasks in Grids. In: Kersting, K., Toussaint, M. (eds.) 6th Starting AI Researchers’ Symposium. Frontiers in Artificial Intelligence and Applications, vol. 241, pp. 125–136. IOS Press (2012)

    Google Scholar 

  8. He, M., Leung, H.F., Jennings, N.R.: A fuzzy-logic based bidding strategy for autonomous agents in continuous double auctions. IEEE Trans. Knowl. Data Eng. 15(6), 1345–1363 (2003)

    Article  Google Scholar 

  9. He, M., Rogers, A., Luo, X., Jennings, N.R.: Designing a successful trading agent for supply chain management. In: 5th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2006, pp. 1159–1166. ACM, New York (2006)

    Google Scholar 

  10. Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using Bayesian learning. In: 7th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2008. pp. 331–338. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2008)

    Google Scholar 

  11. Hou, C.: Predicting agents tactics in automated negotiation. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 127–133 (2004)

    Google Scholar 

  12. Jennings, N.R., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Auton. Agents Multi-Agent Syst. 1(1), 7–38 (1998)

    Article  Google Scholar 

  13. Kawaguchi, S., Fujita, K., Ito, T.: Compromising strategy based on estimated maximum utility for automated negotiation agents competition (ANAC-10). In: Mehrotra, K., Mohan, C., Oh, J., Varshney, P., Ali, M. (eds.) Modern Approaches in Applied Intelligence. LNCS, vol. 6704, pp. 501–510. Springer, Berlin (2011)

    Chapter  Google Scholar 

  14. Kounev, S., Nou, R., Torres, J.: Autonomic QoS-aware resource management in grid computing using online performance models. In: 2nd International Conference on Performance Evaluation Methodologies and Tools. ValueTools 2007, vol. 48, pp. 1–10. Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, Brussels (2007)

    Google Scholar 

  15. Lang, F.: Developing dynamic strategies for multi-issue automated contracting in the agent based commercial grid. In: 5th IEEE International Symposium on Cluster Computing and the Grid. CCGrid 2005, vol. 1, pp. 342–349. IEEE Computer Society, Los Alamitos (2005)

    Google Scholar 

  16. Li, J., Yahyapour, R.: Learning-based negotiation strategies for grid scheduling. In: 6th IEEE International Symposium on Cluster Computing and the Grid. CCGrid 2006, vol. 1, pp. 576–583. IEEE Computer Society, Washington (2006)

    Google Scholar 

  17. Mamdani, E.H.: Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Comput. C-26(12), 1182–1191 (1977)

    Article  Google Scholar 

  18. Narayanan, V., Jennings, N.R.: An adaptive bilateral negotiation model for e-commerce settings. In: 7th International IEEE Conference on E-Commerce Technology, CEC 2005, pp. 34–39. IEEE Computer Society, Washington (2005)

    Google Scholar 

  19. Narayanan, V., Jennings, N.: Learning to negotiate optimally in non-stationary environments. In: Klusch, M., Rovatsos, M., Payne, T. (eds.) Cooperative Information Agents X. LNCS, vol. 4149, pp. 288–300. Springer, Berlin (2006)

    Chapter  Google Scholar 

  20. Nudd, G.R., Kerbyson, D.J., Papaefstathiou, E., Perry, S.C., Harper, J.S., Wilcox, D.V.: PACE-a toolset for the performance prediction of parallel and distributed systems. High Perform. Comput. Appl. 14(3), 228–251 (2000)

    Article  Google Scholar 

  21. Pan, L., Luo, X., Meng, X., Miao, C., He, M., Guo, X.: A two-stage win-win multiattribute negotiation model: optimization and concession. Comput. Intell. 29(4), 577–626 (2013)

    Article  MathSciNet  Google Scholar 

  22. Rubinstein, A.: Perfect equilibrium in a bargaining model. Econometrica 50(1), 97–109 (1982)

    Article  MathSciNet  Google Scholar 

  23. Runkler, T.A., Glesner, M.: DECADE—fast centroid approximation defuzzification for real time fuzzy control applications. In: 1994 ACM Symposium on Applied Computing, pp. 161–165. ACM, New York (1994)

    Chapter  Google Scholar 

  24. Shen, W., Li, Y., Ghenniwa, H.H., Wang, C.: Adaptive negotiation for agent-based grid computing. In: AAMAS 2002 Workshop on Agentcities: Challenges in Open Agent Environments. pp. 32–36. Bologna, Italy (2002)

    Google Scholar 

  25. Silaghi, G.C., Şerban, L.D., Litan, C.M.: A time-constrained SLA negotiation strategy in competitive computational grids. Future Gener. Comput. Syst. 28(8), 1303–1315 (2012)

    Article  Google Scholar 

  26. Sim, K.M.: From market-driven e-negotiation to market-driven g-negotiation. In: 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service, EEE 2005, pp. 408–413. IEEE Computer Society, Washington (2005)

    Google Scholar 

  27. Sim, K.M., Guo, Y., Shi, B.: BLGAN: Bayesian learning and genetic algorithm for supporting negotiation with incomplete information. IEEE Trans. Syst., Man, Cybern., Part B 39(1), 198–211 (2009)

    Article  Google Scholar 

  28. Spooner, D., Jarvis, S., Cao, J., Saini, S., Nudd, G.: Local grid scheduling techniques using performance prediction. IEE Proc.—Comput. Digit. Tech. 150(2), 87–96 (2003)

    Article  Google Scholar 

  29. Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R.: Using Gaussian processes to optimise concession in complex negotiations against unknown opponents. In: 22nd International Joint Conference on Artificial Intelligence. IJCAI 2011, vol. 1, pp. 432–438. AAAI Press (2011)

    Google Scholar 

  30. Wolski, R., Spring, N.T., Hayes, J.: The network weather service: a distributed resource performance forecasting service for metacomputing. Future Gener. Comput. Syst. 15(5–6), 757–768 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, King’s College London Strand, WC2R 2LS, London, UK

    Valeriia Haberland, Simon Miles & Michael Luck

Authors
  1. Valeriia Haberland
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simon Miles
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Luck
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valeriia Haberland .

Editor information

Editors and Affiliations

  1. Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Tokyo, Japan

    Katsuhide Fujita

  2. School of Techno-Business Administration, Nagoya Institute of Technology, Nagoya, Japan

    Takayuki Ito

  3. School of Computer Science and Software Engineering, The University of Wollongong, Wollongong, NSW, Australia

    Minjie Zhang

  4. School of Electronics and Computer Science, University of Southampton, Southampton, UK

    Valentin Robu

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Japan

About this chapter

Cite this chapter

Haberland, V., Miles, S., Luck, M. (2015). Adjustable Fuzzy Inference for Adaptive Grid Resource Negotiation. In: Fujita, K., Ito, T., Zhang, M., Robu, V. (eds) Next Frontier in Agent-Based Complex Automated Negotiation. Studies in Computational Intelligence, vol 596. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55525-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-55525-4_3

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-55524-7

  • Online ISBN: 978-4-431-55525-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation