Affinity P2P: A Self-Organizing Content-Based Locality-Aware Collaborative Peer-to-Peer Network Articles uri icon

authors

  • TIRADO MARTIN, JUAN MANUEL
  • HIGUERO ALONSO-MARDONES, DANIEL
  • ISAILA, FLORIN DANIEL
  • CARRETERO PEREZ, JESUS
  • IAMNITCHI, ADRIANA

publication date

  • August 2010

start page

  • 2056

end page

  • 2070

issue

  • 12

volume

  • 54

International Standard Serial Number (ISSN)

  • 1389-1286

Electronic International Standard Serial Number (EISSN)

  • 1872-7069

abstract

  • The last years have brought a dramatic increase in the popularity of collaborative Web 2.0 sites. According to recent evaluations, this phenomenon accounts for a large share of Internet traffic and
    significantly augments the load on the end-servers of Web 2.0 sites. In
    this paper, we show how collaborative classifications extracted from Web
    2.0-like sites can be leveraged in the design of a self-organizing
    peer-to-peer network in order to distribute data in a scalable manner
    while preserving a high-content locality. We propose Affinity P2P
    (AP2P), a novel cluster-based locality-aware self-organizing
    peer-to-peer network. AP2P self-organizes in order to improve content
    locality using a novel affinity-based metric for estimating the distance
    between clusters of nodes sharing similar content. Searches in AP2P are
    directed to the cluster of interests, where a logarithmic-time parallel
    flooding algorithm provides high recall, low latency, and low
    communication overhead. The order of clusters is periodically changed
    using a greedy cluster placement algorithm, which reorganizes clusters
    based on affinity in order to increase the locality of related content.
    The experimental and analytical results demonstrate that the
    locality-aware cluster-based organization of content offers substantial
    benefits, achieving an average latency improvement of 45%, and up to 12%
    increase in search recall.