计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (7): 246-253.doi: 10.11896/jsjkx.220900176

• 计算机网络 • 上一篇    下一篇

基于改进粒子群算法的云数据中心能耗优化任务调度策略

刘陈伟1, 孙鉴1,2, 雷冰冰1,2, 徐涛1, 吴隹伟1   

  1. 1 北方民族大学计算机科学与工程学院 银川 750021
    2 北方民族大学图像图形智能处理国家民委重点实验室 银川 750021
  • 收稿日期:2022-09-19 修回日期:2023-02-08 出版日期:2023-07-15 发布日期:2023-07-05
  • 通讯作者: 孙鉴(2014132@nun.edu.cn)
  • 作者简介:(liuchenwei@tiangong.edu.cn)
  • 基金资助:
    国家自然科学基金(62062002,62102201);宁夏自然科学基金(2022AAC03289,2022AAC03245,2021AAC03034);北方民族大学中央高校基本科研业务费专项资金(FWNX09);北方民族大学校级一般项目(2021XYZJK01)

Task Scheduling Strategy for Energy Consumption Optimization of Cloud Data Center Based on Improved Particle Swarm Algorithm

LIU Chenwei1, SUN Jian1,2, LEI Bingbing1,2, XU Tao1, WU Zhuiwei1   

  1. 1 School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China
    2 Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission,North Minzu University,Yinchuan 750021,China
  • Received:2022-09-19 Revised:2023-02-08 Online:2023-07-15 Published:2023-07-05
  • About author:LIU Chenwei,born in 1995,postgra-duate,is a member of China Computer Federation.His main research interests include cloud computing and task scheduling.SUN Jian,born in 1982,Ph.D,lecturer,master supervisor,is a member of China Computer Federation.His main research interests include big data storage and big data management.   
  • Supported by:
    National Natural Science Foundation of China(62062002,62102201),Natural Science Foundation of Ningxia,China(2022AAC03289,2022AAC03245,2021AAC03034),Fundamental Research Funds for the Central Universities of Ministry of Education of China(FWNX09) and Research Project of North Minzu University(2021XYZJK01).

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摘要： 随着云计算的发展,能耗急剧上升,这进一步限制了云数据中心整体性能的提高,因此能耗问题引起了工业界和学术界的重视。同时,传统粒子群算法被广泛应用于数据中心任务调度问题的求解,但其收敛速度慢、精度低,容易忽略集群能耗问题。为此提出了一种基于反向学习的混沌映射自适应粒子群算法(OAPSO)。首先,采用反向学习的方法产生初始种群,使粒子更加均匀地分布于初始解空间,提高了初始种群的质量;其次,在粒子更新方式中引入非线性递减的动态惯性权重策略,以改变粒子的寻优能力,使局部搜索和全局搜索达到平衡,避免算法陷入局部最优;然后,引入混沌映射策略,在最优解位置进行扰动变异产生新解,提高算法从局部最优中跳出的能力。最后,在Cloudsim平台上对所提算法进行实验验证,结果表明,与PSO,OBL_TP_PSO和SAPSO算法相比,OAPSO算法资源利用率更高,节能效果更好。

关键词: 云数据中心, 任务调度, 粒子群算法, 混沌映射, 能耗优化

Abstract: With the development of cloud computing,energy consumptionhas increased dramatically,which further limits the improvement of the overall performance of the cloud data center,and thus the energy consumption issue has attracted the attention of industry and academia.Meanwhile,traditional particle swarm optimization algorithm(PSO) is widely used to solve data center task scheduling problems,but it has the shortcomings of slow convergence and low accuracy,and it is easy to ignore the cluster energy consumption problem.A chaotic mapping adaptive particle swarm optimization algorithm based on opposition-based lear-ning(OAPSO) is proposed.Firstly,the initial population is generated by the method of opposition-based learning,which makes the particles more evenly distributed in the initial solution space and improves the quality of the initial population.Secondly,a nonlinear decreasing dynamic inertia weight stra-tegy is introduced into the particle updating mode to change the particle optimization ability,so as to balance the local search and global search and avoid the algorithm falling into the local optimal.Thirdly,the chaotic mapping strategy is introduced to generate new solutions by perturbation and mutation at the optimal location,which improves the ability of the algorithm to jump out of the local optimal.Finally,the proposed algorithm is verified by experiments on the Cloudsim platform,and the results show that,compared to PSO,OBL_ TP_PSO and SAPSO,OAPSO algorithm has higher resource utilization and better energy-saving effect.

Key words: Cloud data center, Task scheduling, Particle swarm optimization, Chaotic mapping, Energy consumption optimization

中图分类号: 

  • TP393
[1]MA X S,TAN J,CHEN S Y et al.Research on optimal particle swarm optimization for multi-objective task scheduling in cloud computing[J].Journal of Electronic Measurement and Instrumentation,2020,34(8):133-143.
[2]WANG G Y,WANG Q S,ZHAO T.The improved strategy of shuffled frog leaping algorithm in the resource scheduling of cloud computing [J].Science Technology and Engineering,2018,18(4):297-303.
[3]LI J,WANG X,GUO L,et al.Innovative Data-Centre Cooling Technologies in China-Liquid Cooling Solution[R].China.Copenhagen Centre on Energy Efficiency,2020.
[4]TAO J.Research on sdn-based dynamic management of energy consumption of cloud data center[C]//2021 IEEE 4th Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC).IEEE,2021:1266-1270.
[5]ZHANG C.Research on energy consumption optimization sch-eduling in the cloud data center[D].Dalian:Dalian University of Technology,2021.
[6]ZHOU Z,SHOJAFAR M,ABAWAJY J,et al.IADE:An improved differential evolution algorithm to preserve sustainability in a 6G network[J].IEEE Transactions on Green Communications and Networking,2021,5(4):1747-1760.
[7]DAYARATHNA M,WEN Y,FAN R.Data center energy consumption modeling:A survey[J].IEEE Communications Surveys &Tutorials,2015,18(1):732-794.
[8]OMARA F A,ARAFA M M.Genetic algorithms for task sche-duling problem[M].Berlin:Springer,2009:479-507.
[9]ZHAO C,ZHANG S,LIU Q,et al.Independent tasks scheduling based on genetic algorithm in cloud computing[C]//2009 5th International Conference on Wireless Communications,Networking and Mobile Computing.IEEE,2009:1-4.
[10]CHEN H,ZHU J,ZHU X,et al.Resource-delay-aware scheduling for real-time tasks in clouds[J].Journal of Computer Research and Development,2017,54(2):446-456.
[11]DORIGO M,CARO G D.Ant colony optimization:A new meta-heuristic[C]//Proceedings of 1999 Congress on Evolutionary Computation.Piscataway,NJ:IEEE,1999:1470-1477.
[12]DORIGO M,STÜTZLE T.The ant colony optimization metaheuristic:Algorithms,applications,and advances[M]//Handbook of metaheuristics:International Series in Operations Research & Management Science.Berlin:Springer,2003:250-285.
[13]GLOVER F.Tabu search-part I[J].ORSA Journal on Computing,1989,1(3):190-206.
[14]SHROFF P,WATSON D W,FLANN N S,et al.Genetic simulated annealing for scheduling data-dependent tasks in heterogeneous environments[C]//5th Heterogeneous Computing Workshop(HCW’96).1996:98-117.
[15]GAN G,HUANG T,GAO S.Genetic simulated annealing algorithm for task scheduling based on cloud computing environment[C]//2010 International Conference on Intelligent Computing and Integrated Systems.IEEE,2010:60-63.
[16]KOKILAVANI T,GEORGE AMALARETHINAM D I.Loadbalanced Min-Min algorithm for static Meta Task scheduling in grid computing[J].International Journal of Computer Applications,2011,20(2):42-48.
[17]HUNG T C,HIEU L N,HY P T,et al.MMSIA:improved max-Min scheduling algorithm for load balancing on cloud computing[C]//Proceedings of the 3rd International Conference on Machine Learning and Soft Computing.New York:Association for Computing Machinery,2019:60-64.
[18]EBERHART R,KENNEDY J.Particle swarm optimization[C]//Proceedings of the IEEE International Conference on Neural Networks.1995:1942-1948.
[19]ZHOU Z,LI F,ABAWAJY J H,et al.Improved PSO algorithm integrated with opposition-based learning and tentative perception in networked data centres[J].IEEE Access,2020,8:55872-55880.
[20]WANG X H,LI J J.Hybrid particle swarm optimization withsimulated annealing[C]//Proceedings of 2004 International Conference on Machine Learning and Cybernetics.IEEE,2004,4:2402-2405.
[21]PANDEY S,WU L,GURU S M,et al.A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments[C]//2010 24th IEEE International Conference on Advanced Information Networking and Applications.IEEE,2010:400-407.
[22]RITU G,KUMAR S A.Multi-objective workflow grid scheduling using e-fuzzy dominance sort based discrete particle swarm optimization[J].Journal of Supercomputer,2014,68(2):709-732.
[23]KAUR S,VERMA A.An efficient approach to genetic algo-rithm for task scheduling in cloud computing environment[J].International Journal of Information Technology and Computer Science(IJITCS),2012,4(10):74-79.
[24]LI H H,FU Y W,ZHAN Z H,et al.Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling[C]//2015 IEEE Congress on Evolutionary Computation(CEC).IEEE,2015:870-876.
[25]WANG J L,GONG B,LIU H,et al.Green heterogeneous sche-duling algorithm through deep integration of hardware and software energy saving principles [J].Journal of Software,2021,32(12):3768-3781.
[26]LIANG B,DONG X,WANG Y,et al.A low-power task scheduling algorithm for heterogeneous cloud computing[J].Journal of Supercomputing,2020,76(9):7290-7314.
[27]LI H H,FU Y W,ZHAN Z H,et al.Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling[C]//2015 IEEE Congress on Evolutionary Computation(CEC).IEEE,2015:870-876.
[28]DING Z Q,LIU C,XIONG T G.Virtual machine placement based on load balancing and power consumption[J].Computer &Digital Engineering,2015,43(11):1962-1967.
[29]ZHANG C Y,FU X,QIAO L.Virtual machine placement based on multi-objective optimization in cloud computing environment[J].Computer Applications and Software,2021,38(3):32-38.
[30]YUAN J Z,LIU Y T.Interrelation strategy for virtual machine selection and placement [J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2021,33(1):163-170.
[31]FAN X B,WOLF-DIETRICH W,LUIZ A B.Power provisioning for a warehouse-sized computer[J].ACM SIGARCH Computer Architecture News,2007,35(2):13-23.
[32]AGARWAL M,SRIVASTAVA G M S.Opposition-based lear-ning inspired particle swarm optimization(OPSO) scheme for task scheduling problem in cloud computing[J].Journal of Ambient Intelligence and Humanized Computing,2021,12(10):9855-9875.
[33]GONG B J,FENG Q Q,ZHAO X F.Virtual machine placement algorithm optimizing energy-efficiency of cloud data center[J].Computer Engineering and Design,2018,39(2):527-531.
[34]LIU J.energy-saving algorithm research ofcomputing resources in data center[D].Chengdu:University of Electronic Science and Technology of China,2013.
[35]SUN M.Research on Energy Consumption Optimization Strategy for Green Cloud Computing[D].Nanjing:Nanjing University of Posts and Telecommunications,2017.
[36]BRATTON D,KENNEDY J.Defining a standard for particleswarm optimization[C]//2007 IEEE Swarm Intelligence Symposium.IEEE,2007:120-127.
[37]POLI R,KENNEDY J,BLACKWELL T.Particle swarm optimization[J].Swarm Intelligence,2007,1(1):33-57.
[38]KENNEDY J,EBERHART R C.Particle Swarm Optimization[C]//Proceedings of the IEEE International Conference on Neural Networks.Piscataway:IEEE Press,1995:1942-1948.
[39]ZHANG J Q,XU S W,LI X C et al.Cloud computing task scheduling based on orthogonal adaptive whale optimization [J].Journal of Computer Applications,2022,42(5):1516-1523.
[40]KUMAR M,SHARMA S C.PSO-COGENT:Cost and energy efficient scheduling in cloud environment with deadline constraint[J].Sustainable Computing:Informatics and Systems,2018,19:147-164.
[41]XIN F J.Research of Multi-objective Task Scheduling based on Chaos Cat Swarm Optimization in Cloud Computing[D].Handan:Hebei University of Engineering,2019.
[42]GUO F.Research of Multi-objective Task Scheduling based on Fireworks Algorithm in Cloud Computing[D]Handan:Hebei University of Engineering,2018.
[43]RAHNAMAYAN S,TIZHOOSH H R,SALAMA M M A.Opposition-based Differential Evolution[J].IEEE Trans.on Evolutionary Computation,2008,12(1):64-79.
[44]YU W W,XIE C W.Hybrid Particle Swarm Optimization with Multiply Strategies [J].Computer Science,2018,45(S1):120-123.
[45]WANG J,QIN J T.Improved seagull optimization algorithmbased on chaotic map and t-distributed mutation strategy [J].Application Research of Computers,2022,39(1):170-176,182.
[46]SUN C Y,WANG X W.Multi-objective task scheduling of cloud computing based on MGA-PSO [J].Computer Applications and Software,2021,38(6):212-218.
[47]BI X J,HU S Y.Firefly algorithm with high precision mixed strategy optimized particle filter[J].Journal of Shanghai Jiaotong University,2019,53(2):232-238.
[48]CALHEIROS R N,RANJAN R,BELOGLAZOV A,et al.CloudSim:a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J].Software,Practice and Experience,2011,41(1):23-50.
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