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Swarm-Inspired Routing Algorithms for Unstructured P2P Networks

机译:非结构化P2P网络的群体启发式路由算法

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摘要

Due to extreme complexity in nowadays networks, routing becomes a challenging task. This problem is especially delicate in unstructured P2P networks, as there is neither a global view on the network nor a global address mapping. Although different conventional solutions are commercially available, swarm-intelligent approaches are promising in case of frequently changing conditions in P2P networks. In this article, an approach inspired by Dictyostelium discoideum slime molds and bees with distributive and autonomous properties is proposed. Both bio-mechanisms are “tailored” for routing in unstructured P2P systems, resulting in swarm-inspired routing algorithms, SMNet and BeeNet. They are compared with three swarm-based routing algorithms and two conventional approaches. The benchmarks include parameter sensitivity-, comparative-, statistical- and scalability-analysis. SMNet outperforms the other algorithms in the comparative analysis regarding the average data packet delay, especially for bigger network sizes and data packet traffic levels. Both algorithms show good scalability.
机译:由于当今网络的极端复杂性,路由成为一项艰巨的任务。由于在网络上既没有全局视图,也没有全局地址映射,因此在非结构化P2P网络中,此问题尤其棘手。尽管可以从市场上获得不同的常规解决方案,但是在P2P网络中条件频繁变化的情况下,群体智能方法很有希望。在本文中,提出了一种由盘基网柄菌粘菌和具有分布和自主特性的蜜蜂启发的方法。这两种生物机制都是“量身定制”的,用于在非结构化P2P系统中进行路由,从而产生了灵感来自群的路由算法SMNet和BeeNet。将它们与三种基于群体的路由算法和两种常规方法进行了比较。基准包括参数敏感性,比较性,统计性和可伸缩性分析。在平均数据包延迟的比较分析中,SMNet优于其他算法,尤其是对于较大的网络规模和数据包流量级别。两种算法都显示出良好的可伸缩性。

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