Q-learning based collaborative load balancing using distributed search for unstructured P2P networks

S. Thampi, K. C. Sekaran
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引用次数: 8

Abstract

Peer-to-peer structures are becoming more and more popular and an exhilarating new class of ground-breaking, Internet-based data management systems. Query load balancing is an important problem for the efficient operation of unstructured P2P networks. The key issue is to identify overloaded peers and reassign their loads to others. This paper proposes a novel mobile agent based two-way load balancing technique for dynamic unstructured P2P networks. In this scheme, target peers are selected based on the result of reinforcement learning. Simulation results indicate that our technique manages the load on peers effectively and increases the search performance significantly.
基于q学习的分布式搜索非结构化P2P网络协同负载平衡
点对点结构正变得越来越流行,并且是一种令人振奋的新类型的突破性的、基于internet的数据管理系统。查询负载均衡是保证非结构化P2P网络高效运行的一个重要问题。关键问题是识别过载的节点,并将它们的负载重新分配给其他节点。提出了一种新的基于移动代理的动态非结构化P2P网络双向负载均衡技术。在该方案中,基于强化学习的结果选择目标节点。仿真结果表明,该方法有效地管理了对等节点的负载,显著提高了搜索性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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