Brief announcement: an incentive-compatible capacity assignment algorithm for bulk data distribution using P2P

In recent years, the rapid growth of peer-to-peer (P2P) networks has provided a new paradigm for content distribution. To improve the efficiency of a P2P system, it is important to provide incentives for the peers to participate and contribute their resources. In this work, we address the incentive provisioning problem for distribution of large-volume content in P2P networks, and present a "seeing-is-believing" incentive-compatible mechanism in which a peer will decide how much resources will be assigned to which neighbors based on what it has experienced. The protocol applies a utility-based resource-trading concept where peers will maximize their contributions for a fair or better return, and we show that by adopting this protocol, the system will achieve Cournot Equlibrium. Our protocol is light-weight, completely decentralized, and cheat-proof.

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