Computer Science – Distributed – Parallel – and Cluster Computing
Scientific paper
2010-07-17
Computer Science
Distributed, Parallel, and Cluster Computing
13 pages, 18 figures This paper has been submitted to Computer Networks
Scientific paper
As ISPs begin to cooperate to expose their network locality information as services, e.g., P4P, solutions based on locality information provision for P2P traffic localization will soon approach their capability limits. A natural question is: can we do any better provided that no further locality information improvement can be made? This paper shows how the utility of locality information could be limited by conventional P2P data scheduling algorithms, even as sophisticated as the local rarest first policy. Network coding's simplified data scheduling makes it competent for improving P2P application's throughput. Instead of only using locality information in the topology construction, this paper proposes the locality-aware network coding (LANC) that uses locality information in both the topology construction and downloading decision, and demonstrates its exceptional ability for P2P traffic localization. The randomization introduced by network coding enhances the chance for a peer to find innovative blocks in its neighborhood. Aided by proper locality-awareness, the probability for a peer to get innovative blocks from its proximity will increase as well, resulting in more efficient use of network resources. Extensive simulation results show that LANC can significantly reduce P2P traffic redundancy without sacrificing application-level performance. Aided by the same locality knowledge, the traffic redundancies of LANC in most cases are less than 50\% of the current best approach that does not use network coding.
Cheng Suqi
Zhang Guoqiang
Zhang Guoqing
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