Peer-to-peer system design: incentives and evolution of cooperation

Abstract

The persistence of cooperation is a longstanding problem in the social and biological sciences. Recent advances of peer-to-peer (P2P) networks manifest as a promising platform to experiment and contribute to theories and algorithms on cooperation. In this thesis, by and large, we view P2P systems as an economy in which incentives are critical to stimulate contribution. Indeed, a P2P system can be considered as a society where different behaviors can emerge, and an empirical platform to understand cooperation and mimic evolving population. Specifically, we consider the problem of cooperation from two perspectives. First and foremost, autonomous nodes are strategic and selfish, who are reluctant to cooperate solely for public good. We investigate incentive scheme design for cooperation in P2P live media streaming networks. The general approach of protocol decomposition shows that practical incentives can only be guaranteed by efficient peer selection, due to stringent playback deadlines. Striker strategy is then proposed so as to align the optimal peer selection of heterogeneous nodes with social welfare maximization, the efficiency of which is validated by repeated game modeling and extensive simulations. The hidden philosophy is to coerce non-cooperative peers into cooperation by collectively implementing punishment threats. This is analogous to strikes and coercion implemented by organizations like unions in human society. On the other hand, just as node selfishness, competition and struggle for survival raise another problem for cooperation. Similar to human society and biological systems, we envision that diverse strategies—some are more exploitative, while others more altruistic—could be deployed by selfish participants to compete against interacting nodes and gain performance advantages. In such a variegated environment, our coevolutionary perspective aims to understand cooperation and rationalize the coexistence and success of diverse behaviors. Population games and evolutionary game theory provide analytical tractability, while learning and evolutionary dynamics are proposed to evolve strategies.