SEARCH, REPLICATION AND GROUPING FOR UNSTRUCTURED P2P NETWORKS
SEARCH, REPLICATION AND GROUPING FOR UNSTRUCTURED P2P NETWORKS
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Date
2006-08-31
Authors
Tsoumakos, Dimitrios
Advisor
Roussopoulos, Nicholas
Citation
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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.