点对点桌面网格的服务质量预测

Marcus Carvalho, Renato Miceli, P. D. Maciel, F. Brasileiro, R. Lopes
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引用次数: 6

摘要

点对点(P2P)桌面网格被认为是提高信息技术(IT)基础设施处理能力的一种经济方式。在P2P网格中,一个节点将其空闲资源捐赠给系统中的其他节点,作为交换,当其处理需求超过其本地计算能力时,可以使用其他节点的空闲资源。尽管它们具有成本效益,但在包含P2P桌面网格的IT基础设施上调度处理需求更加困难。这种困难的根源在于P2P桌面网格所提供的服务质量随时间变化很大。本文研究的是P2P桌面网格的服务质量评估问题。我们的研究基于OurGrid系统,该系统实现了一种基于互惠的自主激励机制,称为偏好网络(NoF)。本文提出了一个基于非of激励机制的P2P桌面网格服务质量预测模型。提出的模型能够估计系统中对等节点在未来时刻可用的资源量。本文还利用实测资料进行了模拟实验,对模型的准确性进行了评价。我们的结果表明,在最坏的情况下,所提出的模型能够预测一个节点将从网格中获得多少给定的资源需求,平均预测误差仅为7.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the Quality of Service of a Peer-to-Peer Desktop Grid
Peer-to-peer (P2P) desktop grids have been proposed as an economical way to increase the processing capabilities of information technology (IT) infrastructures. In a P2P grid, a peer donates its idle resources to the other peers in the system, and, in exchange, can use the idle resources of other peers when its processing demand surpasses its local computing capacity. Despite their cost-effectiveness, scheduling of processing demands on IT infrastructures that encompass P2P desktop grids is more difficult. At the root of this difficulty is the fact that the quality of the service provided by P2P desktop grids varies significantly over time. The research we report in this paper tackles the problem of estimating the quality of service of P2P desktop grids. We base our study on the OurGrid system, which implements an autonomous incentive mechanism based on reciprocity, called the Network of Favours (NoF). In this paper we propose a model for predicting the quality of service of a P2P desktop grid that uses the NoF incentive mechanism. The model proposed is able to estimate the amount of resources that is available for a peer in the system at future instants of time. We also evaluate the accuracy of the model by running simulation experiments fed with field data. Our results show that in the worst scenario the proposed model is able to predict how much of a given demand for resources a peer is going to obtain from the grid with a mean prediction error of only 7.2%.
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