面向大型分布式关键基础设施和资源(CKIR)监控的多智能体分布式动态调度

D. B. Megherbi, D. Xu
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引用次数: 5

摘要

在许多反恐应用中,需要保护关键基础设施和资源(CKIR),如运输系统、航空、公路、海运等。在许多此类应用中,需要确保数十万至数百万英里的道路和/或航空公司的安全。为了实现对此类大型CKIR系统的监控,需要开发基于地理和计算分布式的智能多智能体监控系统。本文主要研究了大型多智能体分布式系统中的智能体调度问题。提出了一种基于消息传递接口(MPI)和动态调度算法的分布式动态代理通信体系结构。本文提出的动态多智能体多节点数据感知调度算法的目标是通过动态平衡不同分布式节点之间的计算负载,同时调度智能体尽可能多地在智能体执行/完成任务所需的数据信息驻留的计算节点上运行,从而最小化智能体的系统总执行时间。期望的目标是减少数据传输开销和延迟,从而提高系统的整体计算性能。
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Multi-agent distributed dynamic scheduling for large distributed Critical Key Infrastructures and Resources (CKIR) surveillance and monitoring
In many counterterrorism applications there is a need to protect Critical Key Infrastructures and Resources (CKIR) such as transportation systems, aviation, highway, maritime transportation, to name a few. In many of these applications, there is a need to secure hundreds of thousands to millions of miles of roadways and/or airways. To achieve the monitoring of such large CKIR systems there is a need to develop intelligent geographically and computationally distributed multi-agent based monitoring systems. The main focus of this paper is on issues related to agent scheduling in such a large multi-agent distributed system. We propose an architecture for the distributed dynamic agent communication based on the Message Passing Interface (MPI) and a dynamic scheduling algorithm. The goal of the proposed dynamic multi-agent multi-node data-aware scheduling algorithm is to minimize the system total execution time of the agents by dynamically balancing the computational load among different distributed nodes while scheduling the agents to run as much as possible on the computational nodes where data information, that the agents need to perform/finish their tasks, reside. The desired aim is to reduce data transfer overhead and latency, and therefore increase the overall system computational performance.
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