Username   Password       Forgot your password?  Forgot your username? 

Cloud Task Scheduling Algorithm based on Improved Genetic Algorithm

Volume 13, Number 7, November 2017 - Paper 9  - pp. 1070-1076
DOI: 10.23940/ijpe.17.07.p9.10701076

Hu Yao, Xueliang Fu*, Honghui Li, Gaifang Dong, Jianrong Li

College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, China

(Submitted on July 25, 2017; Revised on August 30, 2017; Accepted on September 15, 2017)


Cloud computing is a new type of business computing model. It is connected through the network and can obtain various applications, data and IT services. The core of cloud computing is task scheduling, and the application of genetic algorithm (GA) in cloud computing task scheduling is also a hot topic in recent years. In this paper, the "three-stage selection method" and the genetic strategy of "total-division-total" are put forward to improve genetic algorithm. Using simulation experiments in cloud computing simulation software named Cloudsim, the experimental results show that comparing with the simple genetic algorithm (SGA), the improved genetic algorithm (IGA) is better than the simple genetic algorithm on completion time, and it is an effective task scheduling algorithm in cloud computing environment.


References: 13

        1. H.W. Fan, “Value Creation and Its Mechanism of Cloud Computing,” Bullentin of Chinese Academy of Sciences, 2(2015)
        2. Hayes, Brian, “Cloud Computing,” Communications of the Acm, 51.7(2008):9-11
        3. Y.H. Hu and X.L. Tang, “A Task Scheduling Algorithm Based on Improved Genetic Algorithm in Cloud Computing Environment,” Computer Technology and Development , 26.10(2016):137-141
        4. J. Jing, “Cloud Computing Task Scheduling Based on Genetic Algorithm,” Telecom World, 1(2016):33-34
        5. Kumar, Pardeep, and A. Verma, “Scheduling Using Improved Genetic Algorithm in Cloud Computing for Independent Tasks,” International Conference on Advances in Computing, Communications and Informatics, 2012:137-142
        6. Kaur, Shaminder, and A. Verma, “An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment,” International Journal of Information Technology & Computer Science, 4.10(2012):159-190
        7. Lakshmi, R. Durga, and N. Srinivasu, “A Dynamic Approach to Task Scheduling in Cloud Computing Using Genetic Algorithm,” Journal of Theoretical & Applied Information Technology (2016)
        8. J.F. Li and J. Peng, “Task Scheduling Algorithm Based on Improved Genetic Algorithm in Cloud Computing Environment,” Journal of Computer Applications, 31.1(2011):184-186
        9. J.T. Ma, Y.E. Chen, G.J. Hu and L.L. Yan, “Research of Task Scheduling Based on Genetic Algorithm Technology in Cloud Computing Environment,” Software Guide, 15.1(2016):51-53
        10. J. Ma, W. Li, T. Fu, L. Yan and G. Hu, “A Novel Dynamic Task Scheduling Algorithm Based on Improved Genetic Algorithm in Cloud Computing,” Wireless Communications, Networking and Applications. Springer India, 2016:184-186
        11. Y.J. Ma and W.X. Yun, “Research Progress of Genetic Algorithm,” Application Research of Computers, 29.4(2012):1201-1206
        12. X.J. Wang, “Study and Application of A Toolkit for Cloud Computing Simulation—CloudSim,” Microcomputer Applications , 29.8(2013):59-61
        13. Y.N. Wang and W.H. Wu, “The Analysis of Simulation Process of Cloudsim 3.0,” Computer Engineering & Software, 6(2015):109-113


              Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

              This site uses encryption for transmitting your passwords.