Swarm-Inspired Routing Algorithms for Unstructured P2P Networks

Swarm-Inspired Routing Algorithms for Unstructured P2P Networks

Vesna Šešum-Čavić, Eva Kuehn, Stefan Zischka
Copyright: © 2018 |Pages: 41
DOI: 10.4018/IJSIR.2018070102
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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.
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1. Introduction

The remarkable power of swarm-intelligence lies in a coordination of all individuals and communication of “knowledge” without any supervisor. Every individual in the population makes local decisions, and acts in a decentralized manner. Using different natural mechanisms of social interaction, the individual agents reach intelligent solutions. From another point of view, in highly dynamic distributed systems, autonomous agents interact without a central control. Therefore, swarm-inspired intelligence could help highly dynamic systems to cope with environmental changes.

The problem of efficient routing nowadays faces many challenges and copes with highly dynamic nature of the Internet. The main task of routing algorithms is to solve the problem of path selection when sending information from one node (source) to another (destination) over multiple hops within a network (Buford & Yu, 2010). An efficient routing1 is especially an important topic in case of unstructured P2P networks, since no global view on the network exists and no address mapping is maintained. A delivery of data packets is neither guaranteed nor bound to a specific upper limit of hops (Buford & Yu, 2010). Therefore, it requires an advanced approach that is able to manage and solve the above-mentioned problems in an autonomous, intelligent manner and that is sufficiently adaptable. Unstructured P2P overlay networks support dynamics very well, but their “week point” is scalability. Therefore, we start with the main research question that concerns the efficacy of an intelligent routing in unstructured P2P overlay networks.

In this paper, we propose the usage of an intelligent routing based on two swarm-mechanisms: the lifecycle of slime molds Dictyostelium discoideum (Dd) and the bee foraging in fully unstructured P2P overlay networks. Our decision to choose these swarm inspired mechanisms in particular was based on the fact that they already showed potential and additionally, the slime molds do not compute the shortest path per se, but the optimal path for the amount of resources involved (Adamatzky, 2015; Šešum-Čavić et al., 2016), which is important for the path-optimization process in routing. The proposed solution combines the unstructured P2P and space-based computing (Kühn, Craß, Joskowicz, Marek, & Scheller, 2013) for intelligent routing, i.e., the routing in unstructured P2P overlay network is realized by using swarm-inspired intelligence, whereas space-based computing is used for the implementation of (sub)spaces in the overlay network serving as an asynchronous communication medium. As the focus is put on the routing algorithms, the underlying software architecture is only very briefly mentioned. Namely, the diversity of routing algorithms and the resulting substantial amount of different options for their implementation make routing algorithms hard to compare fairly. Existing popular network simulators, even when focused on P2P networks offer only a general environment to simulate distributed systems and do not provide a generic abstracted pattern for benchmarking routing algorithms. Our underlying software architecture for unstructured P2P networks enables the fair and systematic benchmarking and comparison of routing algorithms by providing a meaningful component-based abstraction in a form of coordination patterns (Kühn, 2016). It benchmarks routing algorithms in a generic manner, stripped from specific areas of applications, and supports the easy exchangeability of routing algorithms (simply through “plugging”) and different network topology settings through configuration. Note that the framework itself does not solve the routing problem, but serves as a necessary basement for the used algorithms and abstracts the general requirements. Software agents play the roles of artificial species. A pallet of three swarm intelligence-based algorithms (AntNet, BeeHive, slime molds Physarum polycephalum) and two conventional approaches (Gnutella and k-Random Walker) are adapted for unstructured P2P networks, plugged-in and compared with each other.

The novelty and contribution of this paper include:

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