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Open AccessArticle

Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments

Department of Computer and Information Science, University of Macau, Taipa 999078, Macau
School of Computer Science, North China University of Technology, Beijing 100144, China
Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Authors to whom correspondence should be addressed.
Symmetry 2018, 10(5), 168;
Received: 24 April 2018 / Revised: 11 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
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. View Full-Text
Keywords: cloud computing; Infrastructure as a Service; genetic algorithm; task scheduling cloud computing; Infrastructure as a Service; genetic algorithm; task scheduling
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Duan, K.; Fong, S.; Siu, S.W.I.; Song, W.; Guan, S.S.-U. Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments. Symmetry 2018, 10, 168.

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