The effect of the toplolgy adaptation on search performance in overlay network

TL;DR

This study investigates how adapting network topology in peer-to-peer overlay networks improves search performance, comparing guided and blind search methods with different algorithms and topologies through simulation.

Contribution

It demonstrates that topology adaptation enhances search efficiency and results quality in overlay networks, especially with specific search algorithms.

Findings

Topology adaptation improves search speed.

Guided search yields better results than blind search.

Performance gains are consistent across different algorithms.

Abstract

The work presented in this research paper has focused on the effect of network to-pology adaptation on search performance in peer to peer overlay network. Guided search vs. blind search have been studied with the aim of improving the search re-sults and decreasing the time a search message would take to reach the destination. The network has been formulated as a bi-direction graph with vertices represent network nodes and edges represent connections. The level of network subject of this study is on application layer, that means two nodes are connected if they know each other contact addresses. A good example of this kind of network is the social network where all the lower layers are hidden from the end user. Two different search algorithms have been studied under these circumstances, namely: depth first algorithm and breadth first algorithm. Furthermore, the algorithms performance is examined under random topology (scale free network topology) and under topology adaptation. A simulation scenario has been designed to investigate the fidelity of the system and study the suggested solutions. Simulation results have shown that the search algorithms are performing better under topology adaptation in terms of re-sults quality and search time.