A self-similar super-peer overlay construction scheme for super large-scale P2P applications

Hung-Yi Teng, Chien-Nan Lin and Ren-Hung Hwang ()
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Hung-Yi Teng: National Chung Cheng University
Chien-Nan Lin: National Chung Cheng University
Ren-Hung Hwang: National Chung Cheng University

Information Systems Frontiers, 2014, vol. 16, issue 1, No 5, 45-58

Abstract: Abstract Unstructured peer-to-peer (P2P) overlay networks with two-layer hierarchy, comprising an upper layer of super-peers and an underlying layer of ordinary peers, are used to improve the performance of large-scale P2P applications like content distribution and storage. In order to deal with continuous growth of participating peers, a scalable and efficient super-peer overlay topology is essential. However, there is relatively little research conducted on constructing such super-peer overlay topology. In the existed solutions, the number of connections required to be maintained by a super-peer is in direct proportion to the total number of super-peers. For super large-scale P2P applications, i.e. the number of participating peer is over 1,000,000, these solutions are not scalable and impractical. Therefore, in this paper, we propose a scalable hierarchical unstructured P2P system in which a self-similar square network graph (SSNG) is proposed to construct and maintain the super-peer overlay topology adaptively. The SSNG topology is a constant-degree topology in which each node maintains a constant number of neighbor nodes. Moreover, a simple and efficient message forwarding algorithm is presented to ensure each super-peer to receive just one flooding message. The analytical results showed that the proposed SSNG-based overlay is more scalable and efficient than the perfect difference graph (PDG)-based overlay proposed in the literature.

Keywords: Unstructured P2P system; Super-peer overlay; Self-similar; Constant-degree; Forwarding algorithm (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10796-013-9456-3

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