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10.1109/DASC.2014.35guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
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Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing

Published: 24 August 2014 Publication History

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.

Cited By

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  • (2022)Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control ApproachWireless Communications & Mobile Computing10.1155/2022/61252462022Online publication date: 1-Jan-2022
  • (2019)Dynamic Tree Growth Algorithm for Load Scheduling in Cloud Environments2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790014(65-72)Online publication date: 10-Jun-2019
  • (2019)A novel water pressure change optimization technique for solving scheduling problem in cloud computingCluster Computing10.1007/s10586-018-2867-722:2(601-617)Online publication date: 1-Jun-2019
  • Show More Cited By

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Published In

cover image Guide Proceedings
DASC '14: Proceedings of the 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing
August 2014
549 pages
ISBN:9781479950799

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IEEE Computer Society

United States

Publication History

Published: 24 August 2014

Author Tags

  1. cloud computing
  2. double-fitness
  3. genetic algorithm(GA)
  4. load balancing
  5. task scheduling

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Cited By

View all
  • (2022)Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control ApproachWireless Communications & Mobile Computing10.1155/2022/61252462022Online publication date: 1-Jan-2022
  • (2019)Dynamic Tree Growth Algorithm for Load Scheduling in Cloud Environments2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790014(65-72)Online publication date: 10-Jun-2019
  • (2019)A novel water pressure change optimization technique for solving scheduling problem in cloud computingCluster Computing10.1007/s10586-018-2867-722:2(601-617)Online publication date: 1-Jun-2019
  • (2018)A Novel Meta-Heuristic Approach for Load Balancing in Cloud ComputingInternational Journal of Knowledge-Based Organizations10.4018/IJKBO.20180101038:1(29-49)Online publication date: 1-Jan-2018
  • (2017)A Hyper Heuristic Algorithm for Scheduling of Fog NetworksProceedings of the 21st Conference of Open Innovations Association FRUCT10.23919/FRUCT.2017.8250177(148-155)Online publication date: 13-Nov-2017
  • (2017)A survey on load balancing algorithms in cloud computingInternational Journal of Autonomic Computing10.1504/IJAC.2017.0897042:4(366-383)Online publication date: 1-Jan-2017
  • (2017)A review of task scheduling based on meta-heuristics approach in cloud computingKnowledge and Information Systems10.1007/s10115-017-1044-252:1(1-51)Online publication date: 1-Jul-2017
  • (2016)Resource scheduling for infrastructure as a service (IaaS) in cloud computingJournal of Network and Computer Applications10.1016/j.jnca.2016.04.01668:C(173-200)Online publication date: 1-Jun-2016

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