Proceedings of the twenty-second annual symposium on Principles of distributed computing
Comparing economic incentives in peer-to-peer networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Internet economics: Pricing and policies
The price of selfish behavior in bilateral network formation
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
On a generalization of the stable roommates problem
ACM Transactions on Algorithms (TALG)
Minimizing churn in distributed systems
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Efficient replica maintenance for distributed storage systems
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
The stable fixtures problem-A many-to-many extension of stable roommates
Discrete Applied Mathematics
GECON'07 Proceedings of the 4th international conference on Grid economics and business models
High availability in DHTs: erasure coding vs. replication
IPTPS'05 Proceedings of the 4th international conference on Peer-to-Peer Systems
Strategic reasoning about bundling in swarming systems
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
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In this paper we present a realistic model of peer-to-peer backup and storage systems in which users have the ability to selfishly select remote peers they want to exchange data with. In our work, peer characteristics (e.g., on-line availability, dedicated bandwidth) play an important role and are reflected in the model through a single parameter, termed profile. We show that selecting remote peers selfishly, based on their profiles, creates incentives for users to improve their contribution to the system. Our work is based on an extension to the Matching Theory that allows us to formulate a novel game, termed the stable exchange game, in which we shift the algorithmic nature of matching problems to a game theoretic framework. We propose a polynomial-time algorithm to compute (optimal) stable exchanges between peers and show, using an evolutionary game theoretic framework, that even semi-random peer selection strategies, that are easily implementable in practice, can be effective in providing incentives to users in order to improve their profiles.