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Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments
Kairong Duan1; Simon Fong1; Shirley W. I. Siu1; Wei Song2; Steven Sheng-Uei Guan3
2018-05-17
Source PublicationSymmetry
ISSN2073-8994
Volume10Issue:5
Abstract

Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs). When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA). We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.

KeywordCloud Computing InfrAstructure As a Service Genetic Algorithm Task Scheduling
DOI10.3390/sym10050168
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000435196300041
PublisherMDPI
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:9   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorKairong Duan; Simon Fong
Affiliation1.Department of Computer and Information Science, University of Macau, Taipa 999078, Macau
2.School of Computer Science, North China University of Technology, Beijing 100144, China
3.Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Kairong Duan,Simon Fong,Shirley W. I. Siu,et al. Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments[J]. Symmetry,2018,10(5).
APA Kairong Duan,Simon Fong,Shirley W. I. Siu,Wei Song,&Steven Sheng-Uei Guan.(2018).Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments.Symmetry,10(5).
MLA Kairong Duan,et al."Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments".Symmetry 10.5(2018).
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