Why Share in Peer-to-Peer Networks?

Prior theory and empirical work emphasize the enormous free-riding problem facing peer-to-peer (P2P) sharing networks. Nonetheless, many P2P networks thrive. We explore two possible explanations that do not rely on altruism or explicit mechanisms imposed on the network: direct and indirect private incentives for the provision of public goods. The direct incentive is a traffic redistribution effect that advantages the sharing peer. We din this incentive is likely insufficient to motivate equilibrium content sharing in large networks. We then approach P2P networks as a graph-theoretic problem and present sufficient conditions for sharing and free-riding to co-exist due to indirect incentives we call generalized reciprocity.http://deepblue.lib.umich.edu/bitstream/2027.42/60443/1/p2p_icec08.pd

Why Share in Peer-to-Peer Networks

Prior theory and empirical work emphasize the enormous free-riding problem facing peer-to-peer (P2P) sharing networks. Nonetheless, many P2P networks thrive. We explore two possible explanations: private provision of public goods and generalized reciprocity. We investigate a particular form of private incentives to share content: redistributing traffic in the network to the advantage of the sharing peer. Our preliminary model suggests that this incentive is likely insufficient to motivate equilibrium content sharing in large networks. We then approach P2P networks as a graph-theoretic problem and derive sufficient conditions for sharing and free-riding to co-exist in the absence of direct sharing benefits or an explicit incentive mechanism.http://deepblue.lib.umich.edu/bitstream/2027.42/49504/1/NetEcon06-final.pd

A Game Theoretic Analysis of Incentives in Content Production and Sharing over Peer-to-Peer Networks

User-generated content can be distributed at a low cost using peer-to-peer (P2P) networks, but the free-rider problem hinders the utilization of P2P networks. In order to achieve an efficient use of P2P networks, we investigate fundamental issues on incentives in content production and sharing using game theory. We build a basic model to analyze non-cooperative outcomes without an incentive scheme and then use different game formulations derived from the basic model to examine five incentive schemes: cooperative, payment, repeated interaction, intervention, and enforced full sharing. The results of this paper show that 1) cooperative peers share all produced content while non-cooperative peers do not share at all without an incentive scheme; 2) a cooperative scheme allows peers to consume more content than non-cooperative outcomes do; 3) a cooperative outcome can be achieved among non-cooperative peers by introducing an incentive scheme based on payment, repeated interaction, or intervention; and 4) enforced full sharing has ambiguous welfare effects on peers. In addition to describing the solutions of different formulations, we discuss enforcement and informational requirements to implement each solution, aiming to offer a guideline for protocol designers when designing incentive schemes for P2P networks.Comment: 31 pages, 3 figures, 1 tabl

Interest-Based Self-Organizing Peer-to-Peer Networks: A Club Economics Approach

Improving the information retrieval (IR) performance of peer-to-peer networks is an important and challenging problem. Recently, the computer science literature has attempted to address this problem by improving IR search algorithms. However, in peer-to-peer networks, IR performance is determined by both technology and user behavior, and very little attention has been paid in the literature to improving IR performance through incentives to change user behavior. We address this gap by combining the club goods economics literature and the IR literature to propose a next generation file sharing architecture. Using the popular Gnutella 0.6 architecture as context, we conceptualize a Gnutella ultrapeer and its local network of leaf nodes as a "club" (in economic terms). We specify an information retrieval-based utility model for a peer to determine which clubs to join, for a club to manage its membership, and for a club to determine to which other clubs they should connect. We simulate the performance of our model using a unique real-world dataset collected from the Gnutella 0.6 network. These simulations show that our club model accomplishes both performance goals. First, peers are self-organized into communities of interest - in our club model peers are 85% more likely to be able to obtain content from their local club than they are in the current Gnutella 0.6 architecture. Second, peers have increased incentives to share content - our model shows that peers who share can increase their recall performance by nearly five times over the performance offered to free-riders. We also show that the benefits provided by our club model outweigh the added protocol overhead imposed on the network for the most valuable peers

Federating smart cluster energy grids for peer-to-peer energy sharing and trading

With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project

Incentives in peer-to-peer and grid networking

Today, most peer-to-peer networks are based on the assumptionthat the participating nodes are cooperative. Thisworks if the nodes are indifferent or ignorant about the resourcesthey offer, but limits the usability of peer-to-peernetworks to very few scenarios. It specifically excludes theirusage in any non-cooperative peer-to-peer environment, beit Grid networks or mobile ad-hoc networks. By introducingsoft incentives to offer resources to other nodes, we seean overall performance gain in traditional file-sharing networks.We also see soft incentives promoting the convergenceof peer-to-peer and Grid networks, as they increasethe predictability of the participating nodes, and thereforethe reliability of the services provided by the system as awhole. Reliability is what is required by Grid networks, butmissing in peer-to-peer networks