Optimizing distributed data access in grid environments by using artificial intelligence techniques

被引：0
|
作者：
de Mello, Rodrigo F. [1 ]
Andrade Filho, Jose Augusto [1 ]
Dodonov, Evgueni [1 ]
Porfirio Ishii, Renato [2 ]
Yang, Laurence T. [3 ]
机构：
[1] Univ Sao Paulo, ICMC, Dept Comp Sci, Sao Carlos, SP, Brazil
[2] Univ Fed Mato Grosso do Sul, Dept Comp Stat, Campo Grande, MS, Brazil
[3] St Francis Xavier Univ, Antigonish, NS, Canada
来源：
PARALLEL AND DISTRIBUTED PROCESSING AND APPLICATIONS, PROCEEDINGS | 2007年 / 4742卷
关键词：
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This work evaluates two artificial intelligence techniques for file distribution in Grid environments. These techniques are used to access data on independent servers in parallel, in order to improve the performance and maximize the throughput rate. In this work, genetic algorithms and Hopfield neural networks are the techniques used to solve the problem. Both techniques are evaluated for efficiency and performance. Experiments were conduced in environments composed of 32, 256 and 1024 distributed nodes. The results allow to confirm the decreasing in the file access time and that Hopfield neural network offered the best performance, being possible to be applied on Grid environments.
引用
收藏
页码：125 / 136
页数：12
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