UM
Adaptive incremental Genetic Algorithm for task scheduling in cloud environments
Duan K.1; Fong S.1; Siu S.W.I.1; Song W.2; Guan S.S.3
2018-05-01
Source PublicationSymmetry
ISSN20738994
Volume10Issue:5
AbstractCloud 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 Genetic algorithm Infrastructure as a Service Task scheduling
DOI10.3390/sym10050168
URLView the original
Language英語
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Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.North China University of Technology
3.Xi'an Jiaotong-Liverpool University
Recommended Citation
GB/T 7714
Duan K.,Fong S.,Siu S.W.I.,et al. Adaptive incremental Genetic Algorithm for task scheduling in cloud environments[J]. Symmetry,2018,10(5).
APA Duan K.,Fong S.,Siu S.W.I.,Song W.,&Guan S.S..(2018).Adaptive incremental Genetic Algorithm for task scheduling in cloud environments.Symmetry,10(5).
MLA Duan K.,et al."Adaptive incremental Genetic Algorithm for task scheduling in cloud environments".Symmetry 10.5(2018).
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