An improved genetic algorithm for task scheduling in cloud computing

被引：0
|
作者：
Yin, Shuang [1 ]
Ke, Peng [2 ]
Tao, Ling [1 ]
机构：
[1] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan, Hubei, Peoples R China
[2] Wuhan Univ Sci & Technol, Hubei Prov Key Lab Intelligent Informat Proc & Re, Wuhan, Hubei, Peoples R China
来源：
PROCEEDINGS OF THE 2018 13TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2018) | 2018年
关键词：
genetic algorithm; task scheduling; cloud computing; completion cost; load balancing; CROSSOVER; MUTATION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In cloud computing environment, task scheduling is one of the most critical issues to be solved. Efficient task scheduling mechanism not only meets users' requirements but also ensures cloud resources' high utilization, so as to improve the overall performance of the cloud computing environment. Aiming at this problem, a new scheduling algorithm based on double-fitness algorithm-load balancing and task completion cost genetic algorithm(LCGA) is proposed. The scheduling guarantees load balancing and makes task completion cost less. At the same time, this paper brings in not just variance to represent the load among computing workers but weights multi-fitness function. Through the simulation experiment, the proposed algorithm is being compared with the genetic algorithm based on load balancing (LGA) and the genetic algorithm based on task completion cost (CGA). It proves the effectiveness of the scheduling algorithm and the availability of the optimization method.
引用
收藏
页码：526 / 530
页数：5
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