Linli He
ABSTRACT
Designing efficient resource allocation mechanism for computational grids is extremely challenging because the effective agents in computational grids are inherently self-interested due to their different ownerships. Providing incentive for agents to share their resource with others is the key to make computational grids realistic. The global efficiency should be generated through the interactions among agents from the bottom up. In game theory, forming coalition is such a cooperative game among self-interested agents. We develop a distributed resource allocation mechanism for computational grids by forming resource-sharing coalitions among self-interested agents through automated multiparty negotiation. This mechanism is based on a task-oriented mechanism for measuring the economic value of computational resource usage. The simulation results show that the self-interests of agents in computational grids have considerable impact on the decisions of each agent about how to allocate their resource to appropriate tasks.
- Berman, F., Fox, G. and Hey, T. (Editors), Grid Computing: Making The Global Infrastructure a Reality, John Wiley & Sons, 2003. Google Scholar
Digital Library
- Buyya, R. Economic-Based Distributed Resource Management and Scheduling for Grid Computing. Ph.D. Dissertation, 2002.Google Scholar
- Caillou, P., Aknine, S. and Pinson, S., A Multi-Agent Method for Forming and Dynamic Restructuring of Pareto Optimal Coalitions, in proceedings of the First International Joint Conference on Autonomous Agents and Multi-agent Systems, 2002. Google ScholarDigital Library
- Clearwater, S. H.(Editor), Market-Based Control: A Paradigm for distributed resource allocation, World Scientific, 1996. Google ScholarDigital Library
- Foster, I. and Kesselman, C., The Grid: Blueprint for a New Computing Infrastructure, 1st edition, Morgan Kaufmann, 1998. Google ScholarDigital Library
- He, L. and Ioerger, T. R., A Quantitative Model of Capabilities in Multi-Agent Systems, in Proceedings of the International Conference on Artificial Intelligence, 2003.Google Scholar
- He, L. and loerger, T. R., Combining Bundle Search with Buyer Coalition Formation in Electronic Markets: A Distributed Approach through Explicit Negotiation. The Sixth International Conference on Electronic Commerce, 2004. Google ScholarDigital Library
- Kahan, J. P. and Rapoport, A., Theories of Coalition Formation, Lwarence Erlbaum Associates, Inc., London, 1984.Google Scholar
- Kraus, S., Strategic Negotiation in Multiagent Environments, The MIT Press, 2001. Google ScholarDigital Library
- Lan, Z., Taylor, V. and Bryan, G., A Novel Dynamic Load Balancing Scheme for Parallel Systems, Journal of Parallel and Distributed Computing, Vol 62/12, pp. 1763--1781, 2002 Google ScholarDigital Library
- Lerman, K. and Shehory, O., Coalition formation for largescale electronic markets, in proceedings of the International Conference on Multi-Aent Systems, 2000. Google ScholarDigital Library
- Li, C. and Sycara, K., Algorithm for combinatorial coalition formation and payoff division in an electronic marketplace, in proceedings of the First International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS), 2002. Google ScholarDigital Library
- Proter, M. E., Competitive Strategy, The Free Press, New York, 1980.Google Scholar
- Rosenschein, J. S. and Zlotkin, G., Rules of Encounter: Designing Conventions for Automated Negotiation among Computers, The MIT Press, 1994. Google ScholarDigital Library
- Sandholm, T. Making Markets and Democracy Work: A Story of Incentives and Computing, in Proceedings of IJCAI, 2003. Google ScholarDigital Library
- Sandholm, T., Larson, K., Andersson, M., Shehory, O. and Tohme F., Coalition structure generation with worst case guarantees, Artificial Intelligence Journal, Vol (111), 209--238, 1999. Google ScholarDigital Library
- Sandholm, T., Negotiation among Self-Interested Computationally Limited Agents, Ph.D. Dissertation, 1996. Google ScholarDigital Library
- Sandholm, T. and Lesser, V., Leveled Commitment Contracting: A Backtracking Instrument for Multiagent Systems. AI Magazine 23 (3): 89--100, 2002. Google ScholarDigital Library
- Shehory, O. and Kraus, S., Feasible formation of coalitions among autonomous agents in non-super-additive environments, Computational Intelligence, vol. 15(3), 218--251, 1999.Google ScholarCross Ref
- Shehory, O. and Kraus, S., Methods for task allocation via agent coalition formation, Artificial Intelligence Journal, Vol. 101 (1--2), May, pages 165--200, 1998. Google ScholarDigital Library
- Tesfatsion, L., Agent-based computational economics: Growing economies from the bottom up. Artificial Life, 8:55--82, 2002. Google ScholarDigital Library
- Wolski, R., Brevik, J., Plank, J., and Bryan, T., Grid Resource Allocation and Control Using Computational Economies, In Grid Computing: Making the Global Infrastructure a Reality Berman, F, Fox, G., and Hey, T. editors, Wiley and Sons, pp. 747--772, 2003.Google ScholarCross Ref
- Wolski, R., Plank, J. S., Brevik, J. and Bryan, T. Analyzing market-based resource allocation strategies for the computational Grid. International Journal of High Performance Computing Applications, 15(3):258--281, 2001. Google ScholarDigital Library
Recommendations
Coalitions of malicious intelligent agents
In this paper, we propose a model of pro-social and antisocial behaviour within and between agent coalitions. We analyse benevolent, selfish, and malevolent agents as generic types of pro-social and antisocial agents. We show that social attitudes must ...
Fuzzy kernel-stable coalitions between rational agents
AAMAS '03: Proceedings of the second international joint conference on Autonomous agents and multiagent systemsA large variety of solutions exists for the problem of coalition formation among autonomous agents, at the theoretical level within game theory, and at the practical, algorithmic level, within multi-agent systems. However, one major underlying ...
Self-enforcing coalitions with power accumulation
AbstractAgents endowed with power compete for a divisible resource by forming coalitions with other agents. The coalition with the greatest power wins the resource and divides it among its members. The agents’ power increases according to their share of ...
Comments