SEARCH, REPLICATION AND GROUPING FOR UNSTRUCTURED P2P NETWORKS

Abstract

In my dissertation, I present a suite of protocols that assist in efficient content location and distribution in unstructured Peer-to-Peer overlays. The basis of these schemes is their ability to learn from past interactions, increasing their performance with time.

Peer-to-Peer (P2P) networks are gaining increasing attention from both the scientific and the large Internet user community. Popular applications utilizing this new technology offer many attractive features to a growing number of users. P2P systems have two basic functions: Content search and dissemination. Search (or lookup) protocols define how participants locate remotely maintained resources. In data dissemination, users transmit or receive content from single or multiple sites in the network.

P2P applications traditionally operate under purely decentralized and highly dynamic environments. Unstructured systems represent a particularly interesting class of P2P networks. Peers form an overlay in an ad-hoc manner, without any guarantees relative to lookup performance or content availability. Resources are locally maintained, while participants have limited knowledge, usually confined to their immediate neighborhood in the overlay.

My work aims at providing effective and bandwidth-efficient searching and data sharing. A suite of algorithms which provide peers in unstructured P2P overlays with the state necessary in order to efficiently locate, disseminate and replicate objects is presented. The Adaptive Probabilistic Search (APS) scheme utilizes directed walkers to forward queries on a hop-by-hop basis. Peers store success probabilities for each of their neighbors in order to efficiently route towards object holders. AGNO performs implicit grouping of peers according to the demand incentive and utilizes state maintained by APS in order to route messages from content holders towards interested peers, without requiring any subscription process. Finally, the Adaptive Probabilistic REplication (APRE) scheme expands on the state that AGNO builds in order to replicate content inside query intensive areas according to demand.