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arXiv cs/0511053cs.NI
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一种基于模型的可达路由方法

A Model Based Approach to Reachability Routing

Leland Smith, Muthukumar Thirunavukkarasu, Srinidhi Varadarajan, Naren Ramakrishnan

摘要:

网络路由研究的当前方向没有跟上网络架构的最新发展,例如对等网络、传感器网络、ad-hoc无线网络和覆盖网络。所有这些新技术的一个共同特点是存在高度动态的网络拓扑。目前部署的单路径路由协议不能充分应付这种动态,现有的多路径算法的权衡,导致这些网络上的最佳性能。这就需要在设计路由协议时考虑到这些网络的独特特性。在本文中,我们提出了可达性路由的概念,作为解决方案的路由所带来的挑战,这样的动态网络。特别是,我们制定的可达性路由提供了成本敏感的多路径转发,以及在互联网协议(IP)架构的范围内避免环路。这是通过在概率路由框架内应用强化学习来实现的。以下解释我们的设计决策和算法的描述,我们提供了各种网络拓扑结构上的算法的性能评估。结果表明,与其他基于强化学习的路由算法相比,该算法具有一贯的优越性能。

英文摘要:

Current directions in network routing research have not kept pace with the latest developments in network architectures, such as peer-to-peer networks, sensor networks, ad-hoc wireless networks, and overlay networks. A common characteristic among all of these new technologies is the presence of highly dynamic network topologies. Currently deployed single-path routing protocols cannot adequately cope with this dynamism, and existing multi-path algorithms make trade-offs which lead to less than optimal performance on these networks. This drives the need for routing protocols designed with the unique characteristics of these networks in mind. In this paper we propose the notion of reachability routing as a solution to the challenges posed by routing on such dynamic networks. In particular, our formulation of reachability routing provides cost-sensitive multi-path forwarding along with loop avoidance within the confines of the Internet Protocol (IP) architecture. This is achieved through the application of reinforcement learning within a probabilistic routing framework. Following an explanation of our design decisions and a description of the algorithm, we provide an evaluation of the performance of the algorithm on a variety of network topologies. The results show consistently superior performance compared to other reinforcement learning based routing algorithms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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