A new analytical framework for studying protocol diversity in P2P networks

Thanks to years of research and development, current peer-to-peer (P2P) networks are anything but a homogeneous system from a protocol perspective. Specifically, even for the same P2P system (e.g., BitTorrent), a large number of protocol variants have been designed based on game theoretic considerations with the objective to gain performance advantages. We envision that such variants could be deployed by selfish participants and interact with the original prescribed protocol as well as among them. Consequently, a meta-strategic situation - judiciously selection of different protocol variants - will emerge. In this work, we propose a general framework, Migration, based on evolutionary game theory to study the coevolution of peers for selfish protocol selection, and, most importantly, its impact on system performance. We apply Migration to P2P systems and draw on extensive simulations to characterize the dynamics of selfish protocol selection. The revealed evolution patterns shed light on both theoretical study and practical system design. © 2013 IEEE.published_or_final_versio

Towards the Coevolution of Incentives in BitTorrent

BitTorrent is a peer-to-peer file sharing system that is open to variant behavior at the peer level through modification of the client software. A number of different variants have been released and proposed. Some are successful and become widely used whereas others remain in a small minority or are not used at all. In previous work we explored the performance of a large set of client variants over a number of dimensions by applying Axelrod’s round-robin pairwise tournament approach. However, this approach does not capture the dynamics of client change over time within pairwise tournaments. In this work we extend the tournament approach to include a limited evolutionary step, within the pairwise tournaments, in which peers copy their opponents strategy (client variant) if it outperforms their own and also spontaneously change to the opponents strategy with a low mutation probability. We apply a number of different evolutionary algorithms and compare them with the previous non-evolutionary tournament results. We find that in most cases cooperative (sharing) strategies outperformed free riding strategies. These results are comparable to those previously obtained using the round-robin approach without evolution. We selected this limited form of evolution as a step towards understanding the full coevolutionary dynamics that would result from evolution between a large space of client variants in a shared population rather than just pairs of variants. We conclude with a discussion on how such future work might proceed. © 2015, Budapest Tech Polytechnical Institution. All rights reserved