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HASH-BASED OVERLAY PARTITIONING IN UNSTRUCTURED PEER-TO-PEER SYSTEMS

Authors: Papadakis, Charis; Fragopoulou, Paraskevi; Markatos, Evangelos P.; Dikaiakos, Marios D.; Labrinidis, Alexandros; Papadakis, Charis; Fragopoulou, Paraskevi; +3 Authors

HASH-BASED OVERLAY PARTITIONING IN UNSTRUCTURED PEER-TO-PEER SYSTEMS

Abstract

Unstructured peer-to-peer (P2P) networks suffer from the increased volume of traffic produced by flooding. Methods such as random walks or dynamic querying managed to limit the traffic at the cost of reduced network coverage. In this paper, we propose a partitioning method of the unstructured overlay network into a relative small number of distinct subnetworks. The partitioning is driven by the categorization of keywords based on a uniform hash function. The method proposed in this paper is easy to implement and results in significant benefit for the blind flood method. Each search is restricted to a certain partition of the initial overlay network and as a result it is much more targeted. Last but not least, the search accuracy is not sacrificed to the least since all related content is searched. The benefit of the proposed method is demonstrated with extensive simulation results, which show that the overhead for the implementation and maintenance of this system is minimal compared to the resulted benefit in traffic reduction.

Keywords

Unstructured peer to peers, Overlay networks, Sub-networks, Related contents, Network coverages, Overlay network, Unstructured peer-to-peer systems, Search accuracies, Unstructured overlay networks, Extensive simulations, Flooding, Peer-to-peer, Traffic reductions, Client server computer systems, Resource location, Partitioning methods, Random walks

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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