NDLI: Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing

Content Provider	IEEE Xplore Digital Library
Author	Tingting Wang Zhaobin Liu Yi Chen Yujie Xu Xiaoming Dai
Copyright Year	2014
Description	Author affiliation: Sch. of Sci., Dalian Jiaotong Univ., Dalian, China (Xiaoming Dai) \|\| Sch. of Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China (Tingting Wang; Zhaobin Liu; Yi Chen; Yujie Xu)
Abstract	Task scheduling is one of the most critical issues on cloud platform. The number of users is huge and data volume is tremendous. Requests of asset sharing and reuse become more and more imperative. Efficient task scheduling mechanism should meet users' requirements and improve the resource utilization, so as to enhance the overall performance of the cloud computing environment. In order to solve this problem, considering the new characteristics of cloud computing and original adaptive genetic algorithm(AGA), a new scheduling algorithm based on double-fitness adaptive algorithm-job spanning time and load balancing genetic algorithm(JLGA) is established. This strategy not only works out a tasks scheduling sequence with shorter job and average job makespan, but also satisfies inter-nodes load balancing. At the same time, this paper adopts greedy algorithm to initialize the population, brings in variance to describe the load intensive among nodes, weights multi-fitness function. We then compare the performance of JLGA with AGA through simulations. It proves the validity of the scheduling algorithm and the effectiveness of the optimization method.
Sponsorship	IEEE Comput. Soc.
Starting Page	146
Ending Page	152
File Size	275985
Page Count	7
File Format	PDF
e-ISBN	9781479950799
DOI	10.1109/DASC.2014.35
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cloud computing genetic algorithm(GA) double-fitness Processor scheduling load balancing Sociology Load management task scheduling Resource management cloud computing Statistics Genetic algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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