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An incentive mechanism for message relaying in unstructured peer-to-peer systems

机译:在非结构化对等系统中消息中继的激励机制

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摘要

Distributed message relaying is an important function of a peer-to-peer system to discover service providers. Existing search protocols in unstructured peer-to-peer systems create huge burden on communications, cause long response time, or result in unreliable performance. Moreover, with self-interested peers, these systems are vulnerable to the free-riding problem. In this paper we present an incentive mechanism that not only mitigates the free-riding problem, but also achieves good system efficiency in message relaying for peer discovery. In this mechanism promised rewards are passed along the message propagation process. A peer is rewarded if a service provider is found via a relaying path that includes this peer. The mechanism allows peers to rationally trade-off communication efficiency and reliability while maintaining information locality. We provide some analytic insights to the symmetric Nash equilibrium strategies of this game, and an approximate approach to calculate this equilibrium. Experiments show that this incentive mechanism brings a system utility generally higher than breadth-first search and random walks, based on both the estimated utility from our approximate equilibrium and the utility generated from learning in the incentive mechanism.
机译:分布式消息中继是点对点系统发现服务提供商的重要功能。非结构化对等系统中的现有搜索协议给通信造成了巨大负担,导致响应时间长,或导致性能不可靠。而且,对于有私心的同伴,这些系统容易受到搭便车问题的困扰。在本文中,我们提出了一种激励机制,该机制不仅可以缓解搭便车问题,而且可以在对等点发现的消息中继中实现良好的系统效率。在这种机制中,承诺的奖励将沿着消息传播过程传递。如果通过包含该对等方的中继路径找到了服务提供商,则该对等方将获得奖励。该机制允许对等方在保持信息局部性的同时合理地权衡通信效率和可靠性。我们提供了该博弈的对称纳什均衡策略的一些分析见解,以及一种计算该均衡的近似方法。实验表明,基于我们的近似均衡估计的效用和激励机制中学习产生的效用,这种激励机制带来的系统效用通常高于广度优先搜索和随机游走。

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