`Q-Feed' - An Effective Solution for the Free-riding Problem in Unstructured P2P Networks

TL;DR

This paper introduces Q-Feed, a Q-learning based approach to mitigate free-riding in unstructured P2P networks by incentivizing participation and improving file availability through autonomous replication.

Contribution

It proposes a novel Q-learning based free-riding control mechanism combined with autonomous replication to enhance P2P network performance and fairness.

Findings

Effective reduction of free-rider services

Encourages low-performing nodes to improve participation

Significant decrease in network traffic

Abstract

This paper presents a solution for reducing the ill effects of free-riders in decentralised unstructured P2P networks. An autonomous replication scheme is proposed to improve the availability and enhance system performance. Q-learning is widely employed in different situations to improve the accuracy in decision making by each peer. Based on the performance of neighbours of a peer, every neighbour is awarded different levels of ranks. At the same time a low-performing node is allowed to improve its rank in different ways. Simulation results show that Q-learning based free riding control mechanism effectively limits the services received by free-riders and also encourages the low-performing neighbours to improve their position. The popular files are autonomously replicated to nodes possessing required parameters. Due to this improvement of quantity of popular files, free riders are given opportunity to lift their position for active participation in the network for sharing files. Q-feed effectively manages queries from free riders and reduces network traffic significantly