Fast learning of optimal connections in a peer-to-peer network

Peer-to-Peer (P2P) protocol design is widely undertaken with the assumptions that peers and network connections are homogeneous resources. In practice this assumption is untrue. Furthermore, while there are some P2P networks that provide asymptotic cost optimal topology, or routing, very few existin...

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Bibliographic Details
Main Authors: Harwood, A., Tanin, E., Truong, M.H.
Format: Conference Proceeding
Language:English
Subjects:
Computer science
Cost function
Delay
Intelligent networks
Internet
Network topology
Peer to peer computing
Routing protocols
Software engineering
Telecommunication traffic
Online Access:Request full text
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Description
Summary:Peer-to-Peer (P2P) protocol design is widely undertaken with the assumptions that peers and network connections are homogeneous resources. In practice this assumption is untrue. Furthermore, while there are some P2P networks that provide asymptotic cost optimal topology, or routing, very few existing protocols combine topology optimization and resource optimization, the resulting performance is resource oblivious. We propose a class of traffic based learning protocols, called FLOC protocols, that learn new connections between neighbors of neighbors. For an average routing table size of d and n peers, our protocol is seen to quickly converge from an inefficient network to an asymptotic cost optimal diameter of O(logd n), and simultaneously reduce network delay by approximately 50% in highly heterogeneous networks. We provide extensive simulation results to show FLOC's behavior.
ISSN:1531-2216
2332-5798
DOI:10.1109/ICON.2004.1409147