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Virtual Machine Based on Genetic Algorithm Used in Time and Power Oriented Cloud Computing Task Scheduling

Tongmao Ma1,2, Shanchen Pang1, Weiguang Zhang1, Shaohua Hao1

1 College of Computer and Communication Engineering, China University of Petroleum, Qingdao Shandong, China, 266580
2 Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid. Campus de Montegancedo, Boadilla del Monte (Madrid), Spain, 28660

* Corresponding Author: Shanchen Pang, email

Intelligent Automation & Soft Computing 2019, 25(3), 605-613. https://doi.org/10.31209/2019.100000115

Abstract

In cloud computing, task scheduling is a challenging problem in cloud data center, and there are many different kinds of task scheduling strategies. A good scheduling strategy can bring good effectiveness, where plenty of parameters should be regulated to achieve acceptable performance of cloud computing platform. In this work, combined elitist strategy, three parameters values oriented genetic algorithms are proposed. Specifically, a model built by Generalized Stochastic Petri Nets (GSPN) is introduced to describe the process of scheduling in cloud datacenter, and then the workflow of the algorithms is showed. After that, the effectiveness of the algorithms is found to be valid by the simulations on CloudSim.

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APA Style
Ma, T., Pang, S., Zhang, W., Hao, S. (2019). Virtual machine based on genetic algorithm used in time and power oriented cloud computing task scheduling. Intelligent Automation & Soft Computing, 25(3), 605-613. https://doi.org/10.31209/2019.100000115
Vancouver Style
Ma T, Pang S, Zhang W, Hao S. Virtual machine based on genetic algorithm used in time and power oriented cloud computing task scheduling. Intell Automat Soft Comput . 2019;25(3):605-613 https://doi.org/10.31209/2019.100000115
IEEE Style
T. Ma, S. Pang, W. Zhang, and S. Hao "Virtual Machine Based on Genetic Algorithm Used in Time and Power Oriented Cloud Computing Task Scheduling," Intell. Automat. Soft Comput. , vol. 25, no. 3, pp. 605-613. 2019. https://doi.org/10.31209/2019.100000115



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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