Title :
Genetic algorithm and ant colony algorithm based Energy-Efficient Task Scheduling
Author :
Jianfeng Zhao ; Hongze Qiu
Author_Institution :
Sch. of Comput. Sci. & Technol., Shandong Univ., Jinan, China
Abstract :
Task Scheduling is a critical problem in cloud computing platforms. Traditional algorithms mainly focus on shortening the makespan, but seldom mention the energy consumption. This paper proposes a duplication based method to reach multiple targets, including reducing the executing time and energy cost. The main algorithms used are genetic algorithm and ant colony algorithm, with a new dynamic fusion strategy proposed to gain the optimal solution quickly.
Keywords :
ant colony optimisation; cloud computing; energy consumption; genetic algorithms; scheduling; ant colony algorithm; cloud computing; duplication based method; dynamic fusion strategy; energy consumption; energy cost; energy-efficient task scheduling; genetic algorithm; Biological cells; Computational modeling; Energy consumption; Genetic algorithms; Heuristic algorithms; Processor scheduling; Sociology;
Conference_Titel :
Information Science and Technology (ICIST), 2013 International Conference on
Conference_Location :
Yangzhou
Print_ISBN :
978-1-4673-5137-9
DOI :
10.1109/ICIST.2013.6747695