Hybrid Job Scheduling Algorithm for Cloud Computing Environment

被引：50

作者：

Javanmardi, Saeed ^{[1
]}

Shojafar, Mohammad ^{[2
]}

Amendola, Danilo ^{[2
]}

Cordeschi, Nicola ^{[2
]}

Liu, Hongbo ^{[3
]}

Abraham, Ajith ^{[4
,5
]}

机构：

[1] Islamic Azad Univ, Dept Comp Engn, Dezful Branch, Dezful, Iran

[2] Univ Sapienza Rome, Dept Informat Engn Elect & Telecommunicat DIET, Rome, Italy

[3] Dalian Maritime Univ, Sch Informat, Dalian 116026, Peoples R China

[4] VSB Tech Univ Ostrava, IT4Innovat, Ostrava, Czech Republic

[5] Machine Intelligence Res Labs MIR Labs, Sci Network Innovat & Res Excellence, Auburn, WA 98071 USA

来源：

PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS (IBICA 2014) | 2014年 / 303卷

关键词：

Cloud computing; Scheduling; Genetic algorithm; fuzzy theory; Makespan;

D O I：

10.1007/978-3-319-08156-4_5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost. We try to modify the standard Genetic algorithm and to reduce the iteration of creating population with the aid of fuzzy theory. The main goal of this research is to assign the jobs to the resources with considering the VM MIPS and length of jobs. The new algorithm assigns the jobs to the resources with considering the job length and resources capacities. We evaluate the performance of our approach with some famous cloud scheduling models. The results of the experiments show the efficiency of the proposed approach in term of execution time, execution cost and average Degree of Imbalance (DI).

引用

页码：43 / 52

页数：10

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