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Dividing Agents on the Grid for Large Scale Simulation

机译:网格上的分割代理以进行大规模仿真

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Multi-agent based simulation is an important methodology that uses models incorporating agents to evaluate research conclusions. When the simulation involves a large number of agent, however, it requires extensively high computational power. In that case, all agents in the simulation model should be distributed in a way so that agents can be run in parallel on multiple computational nodes to gain the required performance speed up. In this paper, we present a framework for large scale multi-agent based simulation on grid. We have modified the desktop grid platform BOINC for multi-agent based simulation. Assuming that the agents interact locally with the environment, we proposed an approach to divide the agents for grid nodes so that we can keep load balancing for the distributed simulation while optimizing the communication between grid nodes and the grid server. We have implemented the food foraging simulation to evaluate the feasibility of the framework.
机译:基于多主体的模拟是一种重要的方法,它使用包含主体的模型来评估研究结论。但是,当仿真涉及大量代理时,它需要很高的计算能力。在那种情况下,应该以某种方式分布仿真模型中的所有代理,以便可以在多个计算节点上并行运行代理,以加快所需的性能。在本文中,我们提出了一个基于大规模多智能体的网格仿真框架。我们已经修改了桌面网格平台BOINC,以进行基于多主体的仿真。假设代理与环境局部交互,我们提出了一种划分网格节点代理的方法,以便我们可以在优化网格节点与网格服务器之间的通信的同时保持分布式仿真的负载平衡。我们已经实施了觅食模拟,以评估该框架的可行性。

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