Multi swarms for neighbor selection in peer-to-peer overlay networks

Abstract

Peer-to-peer (P2P) topology has a significant influence on the performance, search efficiency and functionality, and scalability of the application. In this paper, we investigate a multi-swarm approach to the problem of neighbor selection (NS) in P2P networks. Particle swarm share some common characteristics with P2P in the dynamic socially environment. Each particle encodes the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. The portion of the adjustment to the velocity influenced by the individual's cognition, the group cognition from multi-swarms, and the social cognition from the whole swarm, makes an important influence on the particles' ergodic and synergetic performance. We also attempt to theoretically prove that the multiswarm optimization algorithm converges with a probability of 1 towards the global optima. The performance of our This work is supported partially by NSFC Grant 60873054.A. Abraham ( ) Machine Egypt approach is evaluated and compared with other two different algorithms. The results indicate that it usually required shorter time to obtain better results than the other considered methods, specially for large scale problems.

Parallel Evolutionary Peer-to-Peer Networking in Realistic Environments

“…In these studies, it was assumed that a file was divided into fragments and fragments for constructing the entire file were downloaded from many nodes, and the goal was to achieve the shortest download time to download the fragments for the entire file. These studies are similar to our present study in optimizing a network topology by a metaheuristics technique, but the optimization in [13,14] can be thought to be off-line optimization, though there was no description on that, and it is a significant difference from our present study.…”

“…A genetic algorithm was used for determining neighboring nodes in a P2P file-sharing network [13]. Also, a particle swarm optimization (PSO) was used for determining them [14]. In these studies, it was assumed that a file was divided into fragments and fragments for constructing the entire file were downloaded from many nodes, and the goal was to achieve the shortest download time to download the fragments for the entire file.…”