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A Distributed Drafting Algorithm for Load Balancing
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Semi-Distributed Load Balancing for Massively Parallel Multicomputer Systems
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Agent technology: foundations, applications, and markets
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Scheduling and Load Balancing in Parallel and Distributed Systems
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On Load Balancing Approaches for Distributed Object Computing Systems
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Expert Systems with Applications: An International Journal
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OTM'07 Proceedings of the 2007 OTM confederated international conference on On the move to meaningful internet systems - Volume Part I
Fluctuated peer selection policy and its performance in large-scale multi-agent systems
Web Intelligence and Agent Systems
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ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
Adaptive agent allocation for massively multi-agent applications
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A collaborative awareness specification to cover load balancing delivery in CSCW grid applications
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part I
Host selection through collective decision
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
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Multiagent computing on a cluster of workstations is widely envisioned to be a powerful paradigm for building useful distributed applications. The agents of the system span across all the machines of a cluster. Just like the case of traditional distributed systems, load balancing becomes an area of concern. With different characteristics between ordinary processes and agents, it is both interesting and useful to investigate whether conventional load-balancing strategies are also applicable and sufficient to cope with the newly emerging needs, such as coping with temporally continuous agents, devising a performance metric for multiagent systems, and taking into account the vast amount of communication and interaction among agent. This paper discusses the above issues with reference to agent properties and load balancing techniques and outlines the space of load-balancing design choices in the arena of multiagent computing. In view of the special agent characteristics, a novel communication-based load-balancing algorithm is proposed, implemented, and evaluated. The proposed algorithm works by associating a credit value with each agent. The credit of an agent depends on its affinity to a machine, its current workload, its communication behavior, and mobility, etc. When a load imbalance occurs, the credits of all agents are examined and an agent with a lower credit value is migrated to relatively lightly loaded machine in the system. Quasi-simulated experiments of this algorithm show load-balancing improvement compared with conventional workload-oriented load-balancing schemes.