Characterization of a Computational Grid as a Complex System

机译：计算网格作为复杂系统的特征

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摘要

On the grid log data of more than 28 million jobs collected during 20 months, we use statistical and data mining methods to examine the relations primarily between users, computing elements and jobs in the network. The results of the large-scale analysis are used for building probabilistic models of the system behaviour. Bayesian Networks are constructed on historical data and are proven to be able to accurately predict abortion and lengths of newly arriving jobs.

机译：在20个月内收集的超过2800万个作业的网格日志数据中，我们使用统计和数据挖掘方法来检查网络中用户，计算元素和作业之间的关系。大规模分析的结果用于建立系统行为的概率模型。贝叶斯网络建立在历史数据的基础之上，并被证明能够准确预测新到工作的流产和工期。

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来源
《Grids meet autonomic computing workshop 2009》|2009年|P.9-18|共10页
会议地点 Barcelona(ES);Barcelona(ES)
作者
Lovro Ilijasic; Lorenza Saitta;
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作者单位

Dipartimento di Informatica Universita degli Studi di Torino Corso Svizzera 185 10149 Torino, Italy;

rnDipartimento di Informatica Universita del Piemonte Orientale Via Bellini 25/G 15100 Alessandria, Italy;

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会议组织
原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
关键词
grid computing; large-scale data mining; bayesian network;

机译：网格计算;大规模数据挖掘贝叶斯网络;

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Characterization of a Computational Grid as a Complex System

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