A new approach to the maximum-flow problem
Journal of the ACM (JACM)
Challenger: a multi-agent system for distributed resource allocation
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Agent-based simulation of dynamic online auctions
Proceedings of the 32nd conference on Winter simulation
Load Balancing Highly Irregular Computations with the Adaptive Factoring
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Beyond the flow decomposition barrier
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
HLA-based Adaptive Distributed Simulation of Wireless Mobile Systems
Proceedings of the seventeenth workshop on Parallel and distributed simulation
A New Adaptive Middleware for Parallel and Distributed Simulation of Dynamically Interacting Systems
DS-RT '04 Proceedings of the 8th IEEE International Symposium on Distributed Simulation and Real-Time Applications
Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
A platform for massive agent-based simulation and its evaluation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A Platform for Massive Agent-Based Simulation and Its Evaluation
Massively Multi-Agent Technology
Computers and Industrial Engineering
Adaptive Message Clustering for Distributed Agent-Based Systems
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
DS-RT '12 Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications
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In recent years the importance of a large-scale Agent-Based Simulation(ABS) that can handle large complex systems is increasing. We developed a large-scale ABS framework on BlueGene, which is a multi-node supercomputer. The ABS processes the agents' communications. When the number of transmissions among the agents is large, the transmission costs seriously affect the performance of the simulation. It is possible to reduce the amount of transmission among the nodes by clustering the agents which communicate heavily with each other. Assuming that an agent is a graph node, and that a data transmission between agents is a graph edge, this problem can be formulated as a Maximum-Flow and Minimum-Cut Problem. In this paper we present an efficient algorithm to find an approximate solution. Our algorithm is reliable, simple, and needs little computation. We demonstrate its beneficial effects with some experiments.