O artigo desenvolvido no LSD/UFCG entitulado "Predicting the Quality of Service of a P2P Desktop Grid" foi apresentado no "4th Workshop on Desktop Grids and Volunteer Computing Systems" (PCGrid 2010) que fez parte da programação do "10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing" (CCGrid 2010), realizado em junho na cidade de Melbourne, na Austrália.
Após a conferência, os autores foram convidados a submeter uma versão estendida do artigo para uma seção especial em "Volunteer Computing and Desktop Grids" do periódico "Future Generation Computer Systems" (FGCS), da Elsevier. A versão estendida ainda está sendo avaliada e em breve os autores serão notificados sobre a aceitação dos artigos.
Abstract do artigo apresentado no PCGrid 2010:
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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Obs: A apresentação abaixo é melhor visualizada se baixada e executada no Power Point.
