A Load Balancing Scheme for Distributed Simulation Based on Multi-agent System

Nowadays, multi-agent techniques are widely used in developing scalable and flexible software applications. Distributed simulation based on multi-agent system is very useful due to the inherent property of scalability and autonomy of agent system. Load balancing is an important issue in distributed simulation which distributes the jobs among the PCs so as to balance the load. This paper proposes a scheme minimizing the simulation time of distributed simulation implemented on multi-agent platform. In order to achieve the goal, the time management agent is employed along with other agents checking the hardware resources and making the best use of them for minimizing the simulation time by dynamic load balancing with the PCs. The results of an experiment indicate that the proposed scheme substantially reduces the simulation time compared to the existing scheme.

[1]  Stephen John Turner,et al.  Large Scale Distributed Simulation on the Grid , 2006 .

[2]  Michael Luck,et al.  Agent technology: Enabling next generation computing , 2003 .

[3]  Hee Yong Youn,et al.  CALM: an intelligent agent-based middleware for community computing , 2006, The Fourth IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems, and the Second International Workshop on Collaborative Computing, Integration, and Assurance (SEUS-WCCIA'06).

[4]  Stephen John Turner,et al.  Agent Communication in Distributed Simulations , 2004, MABS.

[5]  Fan Miao Miao,et al.  On HLA-Based Collaborative Simulation Techniques , 2009, 2009 Eighth International Conference on Grid and Cooperative Computing.

[6]  Han Seungwok,et al.  A Middleware Architecture for Community Computing with Intelligent Agents (日韓合同ワークショップ 1st Korea-Japan Joint Workshop on Ubiquitous Computing and Networking Systems (ubiCNS 2005)) , 2005 .

[7]  Agostino Poggi,et al.  Multiagent Systems , 2006, Intelligenza Artificiale.

[8]  Ingo J. Timm,et al.  A Hybrid Time Management Approach to Agent-Based Simulation , 2006, KI.

[9]  Hee Yong Youn,et al.  A load balancing scheme for multi-agent systems based on agent state and load condition , 2009, 2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.

[10]  Alexis Drogoul,et al.  Multi-agent Based Simulation: Where Are the Agents? , 2002, MABS.

[11]  K. P. Sycara.  Multiagent systems : Special issue on agents , 1998 .

[12]  Gerhard Weiss,et al.  Multiagent Systems , 1999 .

[13]  Shahid H. Bokhari,et al.  Dual Processor Scheduling with Dynamic Reassignment , 1979, IEEE Transactions on Software Engineering.

[14]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part II: Technologies, Standards, and Tools for Building Multi-agent Systems , 2007, IEEE Transactions on Power Systems.

[15]  Fabio Bellifemine,et al.  Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology) , 2007 .

[16]  Stephen John Turner,et al.  Large scale agent-based simulation on the grid , 2008, Future Gener. Comput. Syst..

[17]  Hee Yong Youn,et al.  A Flexible and Scalable Agent Platform for Multi-Agent Systems , 2007 .

[18]  D. B. Megherbi,et al.  An event-driven multi-agent middleware architecture and protocol design for intelligent geographically distributed battlefield training, modeling and simulation , 2011, 2011 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).