Next Article in Journal
Efficient Superpixel-Guided Interactive Image Segmentation Based on Graph Theory
Next Article in Special Issue
Research on Electronic Voltage Transformer for Big Data Background
Previous Article in Journal
Image Denoising via Improved Dictionary Learning with Global Structure and Local Similarity Preservations
Previous Article in Special Issue
Carbon Oxides Gases for Occupancy Counting and Emergency Control in Fog Environment
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(5), 168; https://doi.org/10.3390/sym10050168

Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments

1
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
*
Authors to whom correspondence should be addressed.
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)
Full-Text   |   PDF [292 KB, uploaded 17 May 2018]   |  

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. View Full-Text
Keywords: cloud computing; Infrastructure as a Service; genetic algorithm; task scheduling cloud computing; Infrastructure as a Service; genetic algorithm; task scheduling
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Duan, K.; Fong, S.; Siu, S.W.I.; Song, W.; Guan, S. .-U. Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments. Symmetry 2018, 10, 168.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top