Predicting the Impact of Measures Against P2P Networks on the Transient Behaviors

TL;DR

This paper models the transient dynamics of P2P networks under epidemic-like dissemination, analyzing how measures against illegal content sharing impact network behavior and effectiveness.

Contribution

It introduces extended stochastic epidemic models to predict phase transitions and evaluate counter-measure efficiency in P2P networks.

Findings

Network exhibits phase transition with small parameter changes.

Counter-measures' effectiveness varies significantly near phase transition points.

Modeling provides insights into optimal strategies against illegal P2P content sharing.

Abstract

The paper has two objectives. The first is to study rigorously the transient behavior of some P2P networks whenever information is replicated and disseminated according to epidemic-like dynamics. The second is to use the insight gained from the previous analysis in order to predict how efficient are measures taken against peer-to-peer (P2P) networks. We first introduce a stochastic model which extends a classical epidemic model and characterize the P2P swarm behavior in presence of free riding peers. We then study a second model in which a peer initiates a contact with another peer chosen randomly. In both cases the network is shown to exhibit a phase transition: a small change in the parameters causes a large change in the behavior of the network. We show, in particular, how the phase transition affects measures that content provider networks may take against P2P networks that distribute non-authorized music or books, and what is the efficiency of counter-measures.