Distributed Coordination of Massively Multi-Agent Systems
- 417 Downloads
Coordination is a key problem in massively multi-agent systems. As applications execute on distributed computer systems, coordination mechanisms must scalably bridge the network distance between where decisions are made and where they are to be enforced.
Our work on the CyberOrgs model addresses this challenge by encapsulating distributed multi-agent computations along with computational and communication resources they require (for carrying out the application’s functions as well as for coordinating actions of the agents) plus purchasing power represented by an amount of eCash for acquiring additional resources. Resources are defined in time and space, and are owned by cyberorgs. Resource ownership changes as a result of trade between cyberorgs.
Ownership of resources coupled with an effective and scalable control structure creates a predictable resource environment for multi-agent systems and their coordination mechanisms to execute in. Particularly, the coordination mechanism can reason about the possibility of successful coordinated action based on predictable communication and processing delays.
This paper presents our experience with hierarchical coordination of distributed processor resource for a system of cyberorgs internally distributed across a number of physical nodes. We demonstrate that encapsulation of network resources creates a scalable opportunity for reasoning about distributed coordinated action to support decision making.
Experimental results show that the CyberOrgs based resource-aware approach scalably increases opportunities for successful coordinated distributed actions involving up to 1500 agents (in much larger systems) by reducing the delay in determining their feasibility, as well as helps avoid attempts of infeasible actions.
KeywordsNetwork Resource Coordination Mechanism Physical Node Global Schedule Primitive Operation
Unable to display preview. Download preview PDF.
- 1.Agha, G.: Actors: A Model of Concurrent Computation in Distributed Systems. MIT Press, Cambridge (1986)Google Scholar
- 2.Andreoli, J.-M., Ciancarini, P., Pareschi, R.: Research Directions in Concurrent Object-Oriented Programming. In: Interaction Abstract Machines, pp. 257–280. MIT, Cambridge (1993)Google Scholar
- 3.Bolosky, W.J., Fitzgerald, R.P., Douceur, J.R.: Distributed schedule management in the tiger video fileserver. In: Symposium on Operating Systems Principles, pp. 212–223 (1997)Google Scholar
- 4.Bond, A., Gasser, L. (eds.): Readings in Distributed Artificial Intelligence. Morgan Kaufman Publishers, San Mateo, California (1988)Google Scholar
- 5.Gasser, L.: DAI approaches to coordination. In: Avouris, N.M., Gasser, L. (eds.) Distributed Artificial Intelligence: Theory and Praxis, pp. 31–51. Kluwer Academic, Dordrecht (1992)Google Scholar
- 7.Hewitt, C., de Jong, P.: Open systems. In: Mylopoulos, J., Schmidt, J.W., Brodie, M.L. (eds.) On Conceptual Modeling, ch. 6, pp. 147–164. Springer, Heidelberg (1984)Google Scholar
- 8.Jamali, N., Liu, C.: Reifying control of multi-owned network resources. In: Proc. of the IPDPS Intl Workshop on High-Level Parallel Programming Models and Supportive Environments (March 2007)Google Scholar
- 9.Jamali, N., Zhao, X.: Hierarchical resource usage coordination for large-scale multi-agent systems. In: Ishida, T., Gasser, L., Nakashima, H. (eds.) MMAS 2005. LNCS (LNAI), vol. 3446, pp. 40–54. Springer, Heidelberg (2005)Google Scholar
- 11.Jang, M., Agha, G.: On efficient communication and service agent discovery in multi-agent systems. In: Proc. of the International Workshop on Software Engineering for Large-Scale Multi-Agent Systems (SELMAS 2004), Edinburgh, pp. 27–33 (May 2004)Google Scholar
- 13.Lal, M., Pandey, R.: A scheduling scheme for controlling allocation of CPU resources for mobile programs. J. AAMAS 5(1), 7–43 (2002)Google Scholar