A Performance Analysis of Incentive Mechanisms for Cooperative Computing

Abstract

As more devices gain Internet connectivity, more information needs to be exchanged between them. For instance, cloud servers might disseminate instructions to clients, or sensors in the Internet of Things might send measurements to each other. In such scenarios, information spreads faster when users have an incentive to contribute data to others. While many works have considered this problem in peer-to-peer scenarios, none have rigorously theorized the performance of different design choices for the incentive mechanisms. In particular, different designs have different ways of bootstrapping new users (distributing information to them) and preventing free-riding (receiving information without uploading any in return). We classify incentive mechanisms in terms of reciprocity-, altruism-, and reputation-based algorithms, and then analyze the performance of these three basic and three hybrid algorithms. We show that the algorithms lie along a tradeoff between fairness and efficiency, with altruism and reciprocity at the two extremes. The three hybrids all leverage their component algorithms to achieve similar efficiency. The reputation hybrids are the most fair and can nearly match altruism's bootstrapping speed, but only the reciprocity/reputation hybrid can match reciprocity's zero-tolerance for free-riding. It therefore yields better fairness and efficiency when free-riders are present. We validate these comparisons with extensive experimental results.