DocumentCode :
3580569
Title :
Dynamic Scheduling Strategy for Testing Task in Cloud Computing
Author :
Yang Lou ; Tao Zhang ; Jing Yan ; Kun Li ; Yechun Jiang ; Haipeng Wang ; Jing Cheng
Author_Institution :
Dept. of Software & Microelectron., Northwestern Polytech. Univ., Xi´an, China
fYear :
2014
Firstpage :
633
Lastpage :
636
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; genetic algorithms; scheduling; task analysis; cloud computing; cloud platform; cloudsim; dynamic scheduling strategy; static genetic algorithm; testing task; Biological cells; Dynamic scheduling; Genetic algorithms; Load modeling; Processor scheduling; Testing; cloud computing; dynamic strategy; genetic algorithm; task scheduling; testing task;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Communication Networks (CICN), 2014 International Conference on
Print_ISBN :
978-1-4799-6928-9
Type :
conf
DOI :
10.1109/CICN.2014.141
Filename :
7065561
Link To Document :
بازگشت