基于改进蚁群优化算法的云计算调度方法
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摘要
云计算以其可伸缩性、高可靠性、低成本以及按需服务等诸多特点吸引了无数研究人员和企业的关注,成为了当今时代的热门话题,云计算任务调度在云计算研究领域占有十分重要的地位。论文首先分析了当前云计算任务调度现状,并对任务调度中常用的蚁群算法进行了描述,同时针对传统蚁群算法在云计算任务调度中的不足,提出了基于改进蚁群优化算法的云计算调度方法,在信息素更新和信息素挥发两个方面对蚁群算法进行了改进。最后使用CloudSim进行实验,对算法的可行性进行了分析和验证。
With the characteristics of scalability,high reliability,low cost and on-demand services etc,cloud computing has attracted the attention of numerous researchers and enterprises,and becomes a hot topic in today's era. Cloud computing task scheduling occupies a very important position in cloud computing research field. This paper first analyzes the current situation of cloud computing task scheduling,then describes the ant colony algorithm commonly used in task scheduling,and in view of the shortage of the traditional ant colony task scheduling algorithm in cloud computing,proposes cloud computing scheduling method based on improved ant colony optimization algorithm,improves the way of pheromone updating and pheromone volatilization. Finally,the experiment is carried out using CloudSim,and the feasibility of the algorithm is analyzed and verified.
With the characteristics of scalability,high reliability,low cost and on-demand services etc,cloud computing has attracted the attention of numerous researchers and enterprises,and becomes a hot topic in today's era. Cloud computing task scheduling occupies a very important position in cloud computing research field. This paper first analyzes the current situation of cloud computing task scheduling,then describes the ant colony algorithm commonly used in task scheduling,and in view of the shortage of the traditional ant colony task scheduling algorithm in cloud computing,proposes cloud computing scheduling method based on improved ant colony optimization algorithm,improves the way of pheromone updating and pheromone volatilization. Finally,the experiment is carried out using CloudSim,and the feasibility of the algorithm is analyzed and verified.
引文
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[2]Boss G,Malladi P,Quan D,et al.Cloud computing[J].Judges Journal,2007,3(1):47-68.
[3]Agarwal M,Srivastava G M S.A genetic algorithm inspired task scheduling in cloud computing[C]//International Conference on Computing,Communication and Automation.IEEE,2017:364-367.
[4]魏赟,陈元元.基于改进蚁群算法的云计算任务调度模型[J].计算机工程,2015,41(2):12-16.WEI Yun,CHEN Yuanyuan.Task scheduling model of cloud computing based on improved ant colony algorithm[J].Computer Engineering,2015,41(2):12-16.
[5]黄伟建,郭芳.基于烟花算法的云计算多目标任务调度[J].计算机应用研究,2017(6):1718-1720.HUAN Weijian,GUO Fang.Multi task scheduling of cloud computing based on fireworks algorithm[J].Application Research of Computers,2017(6):1718-1720.
[6]Li K,Xu G,Zhao G,et al.Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization[C]//Chinagrid Conference.IEEE,2011:3-9.
[7]Wang T,Liu Z,Chen Y,et al.Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing[C]//IEEE,International Conference on Dependable,Autonomic and Secure Computing.IEEE Computer Society,2014:146-152.
[8]Sheng X,Li Q.Template-Based Genetic Algorithm for QoS-Aware Task Scheduling in Cloud Computing[C]//International Conference on Advanced Cloud and Big Data.IEEE,2017:25-30.
[9]Liu J,Kit H C,Hamdi M,et al.Stable Round-Robin Scheduling Algorithms for High-Performance Input Queued Switches[C]//Symposium on High PERFOR-MANCE Interconnects Hot Interconnects.IEEE Computer Society,2002:43.
[10]Etminani K,Naghibzadeh M.A Min-Min Max-Min selective algorihtm for grid task scheduling[C]//Ieee/ifip International Conference in Central Asia on Internet.IEEE,2007:1-7.
[11]Yu X,Yu X.A New Grid Computation-Based Min-Min Algorithm[C]//International Conference on Fuzzy Systems and Knowledge Discovery.IEEE Press,2009:43-45.
[12]Abdulal W,Ramachandram S.Reliability-Aware Genetic Scheduling Algorithm in Grid Environment[C]//International Conference on Communication Systems and Network Technologies.IEEE Computer Society,2011:673-677.
[13]Fidanova S.Simulated Annealing for Grid Scheduling Problem[C]//IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing.IEEE,2006:41-45.
[14]Stützle T.Ant Colony Optimization[J].IEEE Computational Intelligence Magazine,2007,1(4):28-39.
[15]Kennedy J,Eberhart R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,1995.Proceedings.IEEE,1995:1942-1948.
[16]Marco Dorigo,Thomas Stutzle.Ant colony optimization[M].London:MIT Press,2004:1-20.
[17]Ku Ruhana Ku-Mahamud,Aniza Mohamed Din,Husna Jamal Abdul Nasir.Enhancement of ant colony optimization for grid load balancing[J].European Journal of Scientific Research,2011,64(1):42-50.