Dynamic task scheduling using a directed neural network

被引：32

作者：

Tripathy, Binodini ^{[1
]}

Dash, Smita ^{[2
]}

Padhy, Sasmita Kumari ^{[3
]}

机构：

[1] KIIT Univ, Bhubaneswar, Orissa, India

[2] SOA Univ, Bhubaneswar, Odisha, India

[3] Natl Inst Technol, Patna, Bihar, India

来源：

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING | 2015年 / 75卷

关键词：

Task scheduling; Directed search optimization; Neural network; ALGORITHM; SYSTEMS;

D O I：

10.1016/j.jpdc.2014.09.015

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This article is based on the problem of work flow scheduling in grid environment of multi-processors. We, in this paper, introduce three novel approaches for the task scheduling problem using recently proposed Directed Search Optimization (DSO). In the first attempt, task scheduling is framed as an optimization problem and solved by DSO. Next, this paper makes use of DSO as a training algorithm to train (a) a three layer Artificial Neural Network (ANN) and then (b) Radial Basis Function Neural Networks (RBFNN). These'DSO trained networks are used for task Scheduling and interstingly yield better performance than contemporary algorithms as evidenced by simulation results. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：101 / 106

页数：6

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