Load-Balancing for Large Scale Situated Agent-based Simulations

• Published in: Procedia Computer Science
• Main Authors: Rihawi, Omar, Secq, Yann, Mathieu, Philippe
• Other Authors: Systèmes Multi-Agents et Comportements (SMAC), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Slawomir Koziel, Leifur Leifsson, Michael Lees, Valeria V. Krzhizhanovskaya, Jack Dongarra and Peter M.A. Sloot
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2015
• Subjects: Load-balancing; Distributed simulations; Large scale simulations; Multi-agent systems; [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]; [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; Iceland
• Online Access: https://hal.inria.fr/hal-01248794; https://doi.org/10.1016/j.procs.2015.05.204

International audience In large scale agent-based simulations, memory and computational power requirements can increase dramatically because of high numbers of agents and interactions. To be able to simulate millions of agents, distributing the simulator on a computer network is promising, but raises some issues like: agents allocation and load-balancing between machines. In this paper, we study the best ways to automatically balance the loads between machines in large scale situations. We study the performance of two different applications with two different distribution approaches, and we show in our experimental results that some applications can automatically adapt the loads between machines and get alone a high performance in large scale simulations with one distribution approach than the other.