@inproceedings{3dc7f8e9e7bb4ec99bd48e97b244822d,
title = "Dynamic scheduling strategy for testing task in cloud computing",
abstract = "In the testing Cloud platform, there exist too many testing tasks waiting for scheduling at the same time. How to design scheduling strategy is really a challenging problem. In this paper, we firstly analyze the relationship between the testing tasks and establish the task relationship model. Based on these analyses, we propose a dynamic task scheduling strategy using genetic algorithm, which not only ensures to get the least execution time but also guarantee load balance the dynamic strategy based on genetic algorithm is being compared with traditional static genetic algorithm on cloudsim the experimental result shows the high the effectiveness of the proposed strategy.",
keywords = "cloud computing, dynamic strategy, genetic algorithm, task scheduling, testing task",
author = "Yang Lou and Tao Zhang and Jing Yan and Kun Li and Yechun Jiang and Haipeng Wang and Jing Cheng",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 6th International Conference on Computational Intelligence and Communication Networks, CICN 2014 ; Conference date: 14-11-2014 Through 16-11-2014",
year = "2014",
month = mar,
day = "23",
doi = "10.1109/CICN.2014.141",
language = "英语",
series = "Proceedings - 2014 6th International Conference on Computational Intelligence and Communication Networks, CICN 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "633--636",
booktitle = "Proceedings - 2014 6th International Conference on Computational Intelligence and Communication Networks, CICN 2014",
}