Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (6): 1670-1674.DOI: 10.11772/j.issn.1001-9081.2017112854

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Resource scheduling algorithm of cloud computing based on ant colony optimization-shuffled frog leading algorithm

CHEN Xuan1, XU Jianwei1, LONG Dan2   

  1. 1. Zhejiang Industry Polytechnic College, Shaoxing Zhejiang 312000, China;
    2. Faculty of Science, Zhejiang University, Hangzhou Zhejiang 310058, China
  • Received:2017-12-06 Revised:2018-02-06 Online:2018-06-10 Published:2018-06-13
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (11426205,LQ18A010003), the Project of Science Technology Bureau of Shaoxing (2015B70013).


陈暄1, 徐见炜1, 龙丹2   

  1. 1. 浙江工业职业技术学院, 浙江 绍兴 312000;
    2. 浙江大学 理学部, 杭州 310058
  • 通讯作者: 陈暄
  • 作者简介:陈暄(1979-),男,江西南昌人,副教授,硕士,主要研究方向:云计算、无线传感;徐见炜(1980-),男,浙江绍兴人,讲师,硕士,主要研究方向:算法设计;龙丹(1975-)男,湖南湘潭人,讲师,博士,主要研究方向:图像处理、算法设计。
  • 基金资助:

Abstract: Aiming at the issue of low efficiency existing in resource scheduling of cloud computing, a new resource scheduling algorithm of cloud computing based on Quality of Service (QoS) was proposed. Firstly, the quality function and convergence factor were used in Ant Colony Optimization (ACO) algorithm to ensure the efficiency of pheromone updating and the feedback factor was set to improve the selection of probability. Secondly, the local search efficiency of Shuffled Frog Leading Algorithm (SFLA) was improved by setting crossover factor and mutation factor in the SFLA. Finally, the local search and global search of the SFLA were introduced for updating in each iteration of ACO algorithm, which improved the efficiency of algorithm. The simulation experimental results of cloud computing show that, compared with the basic ACO algorithm, SFLA, Improved Particle Swarm Optimization (IPSO) algorithm and Improved Artificial Bee Colony algorithm (IABC), the proposed algorithm has advantages in four indexes of QoS:the least completion time, the lowest cost of consumption, the highest satisfaction and the lowest abnormal value. The proposed algorithm can be effectively used in resource scheduling of cloud computing.

Key words: cloud computing, quality function, Ant Colony Optimization (ACO) algorithm, Shuffled Frog Leading Algorithm (SFLA), feedback factor

摘要: 针对云计算资源调度存在效率低的问题,提出了基于服务质量(QoS)的云计算资源调度算法。首先,在蚁群优化(ACO)算法中采用质量函数和收敛因子来保证信息素更新的有效性,设置反馈因子来提高概率的选择;其次,在蛙跳算法(SFLA)中通过交叉因子和变异因子来提高SFLA的局部搜索效率;最后,在ACO算法的每一次迭代中通过引入SFLA的局部搜索和全局搜索进行更新,提高了算法的效率。云计算的仿真实验结果表明,与基本的ACO算法、SFLA、改进后的粒子群优化(IPSO)算法、改进的人工蜂群算法(IABC)相比,所提算法在QoS的4个指标中有最少的完成时间、最低的消耗成本、最高的满意度和最低的异常数值,表明所提算法能够有效地运用在云计算资源调度中。

关键词: 云计算, 质量函数, 蚁群优化算法, 蛙跳算法, 反馈因子

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