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

doi:10.11772/j.issn.1001-9081.2017112854

Abstract

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

CLC Number:

TP393.027

CHEN Xuan, XU Jianwei, LONG Dan. Resource scheduling algorithm of cloud computing based on ant colony optimization-shuffled frog leading algorithm[J]. Journal of Computer Applications, 2018, 38(6): 1670-1674.

陈暄, 徐见炜, 龙丹. 基于蚁群优化蛙跳算法的云计算资源调度算法[J]. 计算机应用, 2018, 38(6): 1670-1674.

References

[1] 林伟伟,齐德昱.云计算资源调度研究综述[J].计算机科学,2012,39(10):1-6.(LIN W W, QI D Y. Survey of resource scheduling in cloud computing[J].Computer Science, 2012, 39(10):1-6.)
[2] MASDARI M, SALEHI F, JALALI M, et al. A survey of PSO-based scheduling algorithms in cloud computing[J]. Journal of Network and Systems Management, 2017, 25(1):122-158.
[3] KUMAR N, PATEL P. Resource management using ANN-PSO techniques in cloud environment[C]//Proceedings of the 2016 International Congresson Information and Communication Technology, AISC 439. Singapore:Springer, 2016:419-428
[4] KUSHWAH V S, GOYAL S K. A basic simulation of ACO algorithm under cloud computing for fault tolerant[C]//Proceedings of the 2016 International Conference on Data Engineering and Communication Technology, AISC 468. Singapore:Springer, 2016:465-472.
[5] RAGMANI A, OMRI A E, ABGHOUR N, et al. A performed load balancing algorithm for public cloud computing using ant colony optimization[C]//Proceedings of the 20162nd International Conference on Cloud Computing Technologies and Applications. Piscataway, NJ:IEEE, 2016:221-228.
[6] SHAHDI-PASHAKI S, TEYMOURIAN E, TAVAKKOLI-MOGHADDAM R. New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm[J]. Computational and Applied Mathematics, 2018, 37(1):693-718.
[7] KECO D, SUBASI A, KEVRIC J. Cloud computing-based parallel genetic algorithm for gene selection in cancer classification[J]. Neural Computing and Applications, 2016:1-10.
[8] 刘卫宁,靳洪兵,刘波.基于改进量子遗传算法的云计算资源调度[J].计算机应用,2013,33(8):2151-2153.(LIU W N, JIN H B, LIU B. Cloud computing resource scheduling based on improved quantum genetic algorithm[J]. Journal of Computer Applications, 2013, 33(8):2151-2153.)
[9] 赵俊普,殷进勇,金同标,等.遗传蚁群算法在云计算资源调度中的应用[J].计算机工程与设计,2017,38(3):693-697.(ZHAO J P, YIN J Y, JIN T B, et al. Application of genetic ant colony algorithm in cloud computing resource scheduling[J]. Computer Engineering and Design, 2017, 38(3):693-697.)
[10] 邹燕飞,刘淑英.改进蚁群算法的云计算资源调度模型[J].吉林大学学报(理学版),2017,55(3):679-683.(ZOU Y F, LIU S Y. Resources scheduling model of cloud computing based on improved ant colony algorithm[J].Journal of Jilin University (Science Edition), 2017, 55(3):679-683.)
[11] 崔雪娇,曾成,徐占然,等.基于贪心算法的云计算资源调度策略[J].微电子学与计算机,2016,33(6):41-43,48.(CUI X J, ZENG C, XU Z R, et al. Resource scheduling strategy in cloud computing based on greedy algorithm[J]. Microelectronics & Computer, 2016, 33(6):41-43, 48.)
[12] 聂清彬,蔡婷,王宁.改进的蚁群算法在云计算资源调度中的应用[J].计算机工程与设计,2016,37(8):2016-2020.(NIE Q B, CAI T, WANG N. Application of improved ant colony algorithm in resource allocation of cloud computing[J]. Computer Engineering and Design, 2016, 37(8):2016-2020.)
[13] 陈黄科,祝江汉,朱晓敏,等.云计算中资源延迟感知的实时任务调度方法[J].计算机研究与发展,2017,54(2):446-456.(CHEN H K, ZHU J H, ZHU X M, et al. Resource-delay-aware scheduling for real-time tasks in clouds[J]. Journal of Computer Research and Development, 2017, 54(2):446-456.)
[14] 任长安,赵巾帼,罗庆云.云计算资源优化问题求解的萤火虫算法[J].吉林大学学报(理学版),2017,55(5):1234-1238.(REN C A, ZHAO J G, LUO Q Y. Glowworm algorithm for solving optimization problem of cloud computing resource[J].Journal of Jilin University (Science Edition), 2017, 55(5):1234-1238.)
[15] 张继荣,陈琛.基于负载均衡的云计算资源分配算法[J].微电子学与计算机,2017,34(9):43-47.(ZHANG J R, CHEN C. Resource allocation algorithm for cloud computing based on load balancing[J]. Microelectronics & Computer, 2017, 34(9):43-47.)
[16] 王英,秦丁,刘杰,等.基于生产函数的效用优化云计算资源调度算法[J].计算机应用研究,2017,34(2):397-400.(WANG Y, QIN D, LIU J, et al. User utility optimization of cloud computing resource scheduling algorithm based on production function[J]. Application Research of Computers, 2017, 34(2):397-400.)
[17] 张凯.云计算平台服务资源均衡调度仿真研究[J].计算机仿真,2017,34(9):424-427.(ZHANG K. Research on cloud computing platform service resource scheduling[J]. Computer Simulation, 2017, 34(9):424-427.)
[18] 张素芹.云计算任务资源高效调度算法研究[J].计算机仿真,2016,33(11):410-413.(ZHANG S Q. Research on the cloud computing task resource scheduling algorithm[J]. Computer Simulation, 2016, 33(11):410-413.)
[19] 周婉.基于云计算的资源调度算法研究[D].北京:北京交通大学,2016:25-26.(ZHOU W. Research on resource scheduling algorithms based on cloud computing environment [D]. Beijing: Beijing Jiaotong University, 2016: 25-26)
[20] 蔡林益.基于粒子群算法的云计算资源配置研究[J].西南师范大学学报(自然科学版),2017,42(9):128-132.(CAI L Y. On cloud computing resource allocation based on particle swarm optimization algorithm [J]. Journal of Southwest China Normal University (Natural Science Edition), 2017, 42(9): 128-132.)
[21] 颜丽燕,张桂珠.基于蜂群算法的多维QoS云计算任务调度[J].计算机工程与科学,2016,38(4):648-655.(YAN L Y, ZHANG G Z. Multi-dimensional QoS cloud task scheduling based on colony algorithm [J]. Computer Engineering and Science, 2016, 38(4): 648-655.)