云环境下基于DO-GAPSO的任务调度算法

摘要/Abstract

摘要： 为了找到合理的云计算任务调度方案,仅从单一方面来优化调度策略已不能满足用户需求,但从多个方面优化调度策略又面临着权重分配问题。针对上述问题,从任务完成时间、任务完成成本、服务质量3个方面考虑,提出一种基于遗传与粒子群算法相融合的动态目标任务调度算法,在算法的适应度评价函数建模中引入线性权重动态分配策略。通过CloudSim平台进行云环境仿真实验,并将此算法与经典的双适应遗传算法(DFGA)、离散粒子群优化算法(DPSO)进行比较。实验结果表明,在相同的设置条件下,该算法在执行效率、寻优能力等方面优于其他两个算法,是一种云计算环境下有效的任务调度算法。

关键词: 惯性权重, 粒子群优化, 任务调度, 遗传算法, 云计算

Abstract: In order to find reasonable cloud computing task scheduling scheme,the demand of users can not be satisfied by optimizing scheduling strategy from a single aspect,and there are some weight assignment problems in several aspects to optimize scheduling policy.Focusing on the problems,considering the completion time,cost and service quality,an algorithm of a dynamic target based on particle swarm and genetic algorithm(DO-GAPSO) was proposed,a dynamic linear weighting allocation policy wasintroduced in the fitness of function modeling.Cloud environment simulation experiment was conducted in the CloudSim platform.Under the same condition,discrete particle swarm optimization(DPSO),double fitness genetic algorithm(DFGA) were compared with the proposed algorithm.The experimental results show that the proposed algorithm is better than the other two algorithms in execution efficiency and optimization ability.It is a kind of effective task scheduling algorithm in cloud computing environment.

Key words: Cloud computing, Genetic algorithm, Inertia weight, Particle swarm optimization, Task scheduling

中图分类号:

TP393

孙敏,陈中雄,卢伟荣. 云环境下基于DO-GAPSO的任务调度算法[J]. 计算机科学, 2018, 45(6A): 300-303. https://doi.org/

SUN Min CHEN, Zhong-xiong, LU Wei-rong. Task Scheduling Algorithm Based on DO-GAPSO under Cloud Environment[J]. Computer Science, 2018, 45(6A): 300-303. https://doi.org/

参考文献

[1]AHMED M,CHOWDHURY A S M R,AHMEDAN M,et al.Advanced survey on cloud computing and state-of-the-art research issues[J].International Journal of Computer Science Issues,2012,9(1):201-207.
[2]封良良,张陶,贾振红,等.云计算环境下基于改进粒子群的任务调度算法[J].计算机工程,2013,39(5):183-186.
[3]邬开俊,鲁怀伟.云环境下基于DPSO的任务调度算法[J].计算机工程,2014,40(1):59-62.
[4]盛小东,李强,刘昭昭.云环境下基于模板遗传算法的任务调度方法[J].计算机应用,2016,36(3):633-636.
[5]ZHANG D,GUAN Z,LIU X.An adaptive particle swarm optimization algorithm and simulation[C]∥IEEE International Conference on Automation & Logistics.2007:2399-2402.
[6]WU M.Research on Improvement of Task Scheduling Algo- rithm in Cloud Computing[J].Applied Mathematics & Information Sciences,2015,9(1):507-516.
[7]SAVITHA P,REDDY J G.A Review Work On Task Scheduling In Cloud Conputing Using GeneticAlgorithm[J].International Journal of Scientific Technology Research,2013,2(8):241-245.
[8]MANDAL T,ACHARYYA S.Optimal Task Scheduling in Cloud Computing Environment:Meta Heuristic Approaches[J].International Conference on Electrical Information and Communication Technology,2016,1(28):24-28.
[9]AGARWAL A,JAIN S.Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment[J].International Journal of Computer Trends & Technology,2014,9(7):344-349.
[10]KHALILI A,BABAMIR S M.Makespan Improvement of PSO-based Dynamic Schedulingin Cloud Environment[J].Electrical Engineering,2015,7(2):613-618.
[11]RANI A,GARG K.A Review on Task Scheduling Algorithmin Cloud Computing Environment[J].International Journal of Scien-tific & Engineering Research,2016,5(4):9724-9729.
[12]RAMEZANI F,LU J,TAHERI J,et al.Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments[J].World Wide Web-internet & Web Information Systems,2015,18(6):1737-1757.
[13]JENA R K.Multi Objective Task Scheduling in Cloud Environment Using Nested PSO Framework[J].Procedia Computer Science,2015,7(57):1219-1227.
[14]LAKSHMI R D,SRINIVASU N.A dynamic approach to task scheduling in cloud computing using genetic algorithm[J].Journal of Theoretical & Applied Information Technology,2016,3(85):124-135.
[15]KAUR S,VERMA A.An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment[J].International Journal of Information Technology & Computer Science,2012,4(10):159-190.
[16]AKILANDESWARI P,SRIMATHI H.Survey and analysis on Task scheduling in Cloud environment[J].Indian Journal of Science & Technology,2016,9(37):974-5645.

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