Meta-Heuristically Seeded Genetic Algorithm for Independent Job Scheduling in Grid Computing

被引：2
|
作者：
Younis, Muhanad Tahrir [1 ]
Yang, Shengxiang [1 ]
Passow, Benjamin [1 ]
机构：
[1] De Montfort Univ, CCI, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England
来源：
APPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2017, PT I | 2017年 / 10199卷
基金：
英国工程与自然科学研究理事会;
关键词：
Meta-heuristic algorithms; Seeded genetic algorithm; Ant colony optimization; Job scheduling; Grid computing; Makespan; TASKS;
D O I：
10.1007/978-3-319-55849-3_12
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Grid computing is an infrastructure which connects geographically distributed computers owned by various organizations allowing their resources, such as computational power and storage capabilities, to be shared, selected, and aggregated. Job scheduling problem is one of the most difficult tasks in grid computing systems. To solve this problem efficiently, new methods are required. In this paper, a seeded genetic algorithm is proposed which uses a meta-heuristic algorithm to generate its initial population. To evaluate the performance of the proposed method in terms of minimizing the makespan, the Expected Time to Compute (ETC) simulation model is used to carry out a number of experiments. The results show that the proposed algorithm performs better than other selected techniques.
引用
收藏
页码：177 / 189
页数：13
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