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TARC: A Novel Topology Adaptation Algorithm Based on Reciprocal Contribution in Unstructured P2P Networks

机译:TARC:一种在非结构化P2P网络中基于对等贡献的新型拓扑自适应算法

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The autonomous behaviors of peers are usually neglected in the topology formation and adaptation in P2P network, thus a large number of Free-rider and Malicious peers would reside in the center of network, which degrades the user perceived QoE and overall network performance. In this paper, a novel and fully distributed approach Topology Adaptation algorithm based on Reciprocal Contribution (TARC) is proposed to address such problem. In our paradigm, the topology would be periodical optimized according to Reciprocal Contribution Capacity (RCC) of peers. The RCC reflects the historical behavior of a peer with regard to its content provision and transmission capability as well as nodes locating capability. Peer that possesses higher RCC, namely contributor, as it were more cooperative would be easily and likely to establish connections with others. On the other hand, malicious and free-riding nodes would be recognized and forced to the marginal of P2P network. It is shown through experiments that TARC effectively suppress the fake services and prevent non-contribution peer from occupying the center of network. Whatȁ9;s more, there is a significant improvement in term of overall search quality and downloading time, for contributing peers in a network that applies our algorithm.
机译:在P2P网络中,拓扑的形成和自适应通常忽略了对等体的自主行为,因此大量的搭便车和恶意对等体将驻留在网络中心,这会降低用户感知的QoE和整体网络性能。本文提出了一种基于互惠贡献(TARC)的新颖且完全分布式的拓扑自适应算法来解决这一问题。在我们的范例中,将根据对等体的互惠贡献能力(RCC)定期优化拓扑。 RCC反映了对等方在其内容提供和传输能力以及节点定位能力方面的历史行为。拥有较高RCC的对等方(即贡献者),因为它更加合作,将很容易并可能与他人建立联系。另一方面,恶意和搭便车的节点将被识别并被迫排在P2P网络的边缘。通过实验表明,TARC有效地抑制了虚假服务,防止了非贡献对等方占据网络的中心。另外,对于使用我们算法的网络中的对等节点,总体搜索质量和下载时间有了显着改善。

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