Using stochastic learning automata for job scheduling in distributed processing systems
Journal of Parallel and Distributed Computing
Analyzing the social behavior of contract net protocol
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Dynamic Load Balancing on Web-Server Systems
IEEE Internet Computing
On Load Balancing for Distributed Multiagent Computing
IEEE Transactions on Parallel and Distributed Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Performance Evaluation of an Agent-Based Resource Management Infrastructure for Grid Computing
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Performance Analysis of Distributed Search in Open Agent Systems
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Distributed Sensor Networks: A Multiagent Perspective
Distributed Sensor Networks: A Multiagent Perspective
Organization-Based Cooperative Coalition Formation
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Exploiting as hierarchy for scalable route selection in multi-homed stub networks
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
lbnamed: A Load Balancing Name Server in Perl
LISA '95 Proceedings of the 9th USENIX conference on System administration
Agent-organized networks for dynamic team formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Total performance by local agent selection strategies in multi-agent systems
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Multi-Agent Systems Performance by Adaptive/Non-Adaptive Agent Selection
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Timed environment for web agents
Web Intelligence and Agent Systems
Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
Grid load balancing using intelligent agents
Future Generation Computer Systems
Using response probability to build system redundancy in multiagent systems
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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This paper describes how, in large-scale multi-agent systems, each agent's adaptive selection of peer agents for collaborative tasks affects the overall performance and how this performance varies with the workload of the system and with fluctuations in the agents' peer selection policies (PSP). An intelligent agent in a multi-agent system (MAS) often has to select appropriate agents to assign tasks that cannot be executed locally. These collaborating agents are usually chosen according to their skills. However, if multiple candidate peer agents still remain a more efficient agent is preferable. Of course, its efficiency is affected by the agent' workload and CPU performance and the available communication bandwidth. Unfortunately, as no agent in an open environment such as the Internet can obtain such data from any other agent, this selection must be done according to the available local information about the other known agents. However, this information is limited, usually uncertain and often obsolete. Agents' states may also change over time, so the PSP must be adaptive to some extent. We investigated how the overall performance of MAS would change under adaptive policies in which agents selects peer agents using statistical/reinforcement learning. We particularly focused on mutual interference for selection under different workloads, that is, underloaded, near-critical, and overloaded situations. This paper presents simulation results and shows that the overall performance of MAS highly depends on the workload. It is shown that when agents' workloads are near the limit of theoretical total capability, a greedy PSP degrades the overall performance, even after a sufficient learning time, but that a PSP with a little fluctuation, called fluctuated PSP, can considerably improve it. This paper is the revised and extended version of our conference papers [21] and [22].