基于双层规划的容器云资源动态配置算法

计算机与现代化 ›› 2022, Vol. 0 ›› Issue (12): 1-5.

• 算法设计与分析 • 下一篇

基于双层规划的容器云资源动态配置算法

(1.河源职业技术学院电子与信息工程学院,广东河源517000;2.广州华商学院数据科学学院,广东广州511300)

出版日期:2023-01-04 发布日期:2023-01-04
作者简介:周永福(1979—),男,江西贵溪人,副教授,硕士,研究方向:网络应用与安全,云计算,E-mail: zhouyongfu_2021@126.com; 通信作者：徐胜超(1980—),男,湖北武汉人,讲师,硕士,研究方向:并行分布式处理软件,E-mail: isdooropen@126.com。
基金资助:
国家自然科学基金青年基金资助项目(61403219); 2021年广东省教育厅重点领域专项课题(2021ZDZX4080)；广州华商学院校级导师制科研项目(2022HSDS16)

Dynamic Allocation Algorithm of Container Cloud Resources Based on Bi-level Programming

(1. Institute of Electronics and Information Engineering, Heyuan Polytechnic, Heyuan 517000, China;
2. School of Data Science, Guangzhou Huashang College, Guangzhou 511300, China)

Online:2023-01-04 Published:2023-01-04

摘要/Abstract

摘要： 本文分析容器云资源动态配置决策问题，通过定义容器云资源的调度任务，求解得到容器云资源调度时间；利用容器云资源调度任务的最短时间矩阵，获取容器云资源调度所需的条件。在双层规划条件下，求解容器云资源调度的目标函数和约束函数；考虑到用户的任务情况和云数据中心的云资源状况，在虚拟机上构建一个到物理主机的矩阵，通过构建容器云资源动态配置结果在优化时的目标函数，结合约束条件，实现容器云资源的动态配置。实验结果表明，资源动态配置算法不仅可以提高容器云资源的利用率，还可以减少配置完成时间，具有更好的动态配置性能。

关键词: 双层规划, 容器云资源, 动态配置, 调度模型, 目标函数

Abstract: The dynamic configuration decision problem of container cloud resources is analyzed in this paper. By defining the scheduling task of container cloud resources, the scheduling time of container source resources is solved. The shortest time matrix of container cloud resource scheduling task is used to obtain the conditions needed for container cloud resource scheduling. Under the bi-level planning condition, the objective function and constraint function of container cloud resource scheduling are solved, and the container cloud resource scheduling model is constructed. Considering the tasks of users and the cloud resources of data centers, a matrix to physical hosts is constructed on virtual machines. By constructing the objective function of container cloud resource dynamic configuration results in optimization, and combining with constraints, the dynamic configuration of container cloud resources is realized. Experimental results show that the proposed algorithm can not only improve the utilization of container cloud resources, but also reduce the configuration completion time, and has better dynamic configuration performance.

Key words: bi-level planning, container cloud resources, dynamic configuration, scheduling model, objective function

周永福, 徐胜超. 基于双层规划的容器云资源动态配置算法[J]. 计算机与现代化, 2022, 0(12): 1-5.

ZHOU Yong-fu, XU Sheng-chao. Dynamic Allocation Algorithm of Container Cloud Resources Based on Bi-level Programming[J]. Computer and Modernization, 2022, 0(12): 1-5.

参考文献

［1］周平,殷波,邱雪松,等. 面向服务可靠性的云资源调度方法［J］. 电子学报, 2019,47(5):1036-1043.
［2］毛莺池,郝帅,平萍,等. 基于组合双向拍卖的云资源调度方法［J］. 计算机应用, 2019,39(1):1-7.
［3］李双刚,张爽,王兴伟. 基于自适应虚拟机迁移的云资源调度机制［J］. 计算机科学, 2020,47(9):238-245.
［4］李启锐,彭志平,崔得龙,等. 容器云环境虚拟资源配置策略的优化［J］. 计算机应用, 2019,39(3):784-789.
［5］张人龙,刘小红. 大数据环境下基于谱机器学习的云物流资源配置［J］. 统计与决策, 2021(9):177-179.
［6］ LOU G X, CAI Z Y. A cloud computing oriented neural network for resource demands and management scheduling［J］. International Journal of Network Security, 2019,21(3):477-482.
［7］高天阳,虞慧群,范贵生. 基于模拟退火遗传算法的云资源调度方法［J］. 华东理工大学学报(自然科学版), 2019,45(3):471-477.
［8］ SREENU K, SREELATHA M. W-scheduler: Whale optimization for task scheduling in cloud computing［J］. Cluster Computing, 2019,22(1):1087-1098.
［9］聂清彬,潘峰,吴嘉诚,等. 基于改进蚁群算法的自适应云资源调度模型研究［J］. 激光与光电子学进展, 2020,57(1):82-88.
［10］TAN B, MA H, MEI Y. Novel genetic algorithm with dual chromosome representation for resource allocation in container-based clouds［C］// 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). 2019:452-456.
［11］PIRAGHAJ S F, DASTJERDI A V, CALHEIROS R N, et al. A framework and algorithm for energy efficient container consolidation in cloud data centers［C］// 2015 IEEE International Conference on Data Science and Data Intensive Systems. 2015:368-375.
［12］VALDEZ M G, GUERVS J J M. A container-based cloud-native architecture for the reproducible execution of multi-population optimization algorithms［J］. Future Generation Computer Systems, 2021,116:234-252.
［13］TAN B, MA H, MEI Y, et al. A cooperative coevolution genetic programming hyper-heuristics approach for on-line resource allocation in container-based clouds［J］. IEEE Transactions on Cloud Computing, 2022,10(3):1500-1514.
［14］龚坤,武永卫,陈康. 容器云多维资源利用率均衡调度研究［J］. 计算机应用研究, 2020,37(4):1102-1106.
［15］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.
［16］SHI T, MA H, CHEN G. Energy-aware container consolidation based on PSO in cloud data centers［C］// 2018 IEEE Congress on Evolutionary Computation (CEC). 2018. DOI:10.1109/CEC.2018.8477708.
［17］NABI S, AHMED M. PSO-RDAL:Particle swarm optimization-based resource- and deadline-aware dynamic load balancer for deadline constrained cloud tasks［J］. The Journal of Supercomputing, 2022,78(4):4624-4654.
［18］BALICKI J. Many-objective quantum-inspired particle swarm optimization algorithm for placement of virtual machines in smart computing cloud［J］. Entropy, 2021,24(1):58.
［19］BANSAL M, MALIK S K. A multi-faceted optimization scheduling framework based on the particle swarm optimization algorithm in cloud computing［J］. Sustainable Computing: Informatics and Systems, 2020,28. DOI:10.1016/j.suscom.2020.100429.
［20］MOHAMED A M, ABDELSALAM H M. A multicriteria optimization model for cloud service provider selection in multicloud environments［J］. Software: Practice and Experience, 2020,50(6):925-947.
［21］刘扬,魏蔚,张伟哲. 随机需求下面向异质费用的云资源调度算法［J］. 哈尔滨工业大学学报, 2018,50(11):116-121.
［22］齐平,王福成,王必晴. 一种基于图模型的可信云资源调度算法［J］. 山东大学学报(理学版), 2018,53(1):63-74.
［23］贾嘉,慕德俊. 基于粒子群优化的云计算低能耗资源调度算法［J］. 西北工业大学学报, 2018,36(2):339-344.
［24］CHOUDHARY A, GUPTA I, SINGH V, et al. A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing［J］. Future Generation Computer Systems, 2018,83:14-26.
［25］ZUO L, DONG S, SHU L, et al. A multiqueue interlacing peak scheduling method based on tasks’ classification in cloud computing［J］. IEEE Systems Journal, 2018,12(2):1518-1530.
［26］VENKATASWAMY S B, MANDAL I, KESHAVARAO S.ChicWhale optimization algorithm for the VM migration in cloud computing platform［J］. Evolutionary Intelligence, 2020,13(5). DOI:10.1007/s12065-020-00386-9.
［27］DEVARAJ A F S, ELHOSENY M, DHANASEKARAN S, et al. Hybridization of firefly and improved multi-objective particle swarm optimization algorithm for energy efficient load balancing in cloud computing environments［J］. Journal of Parallel and Distributed Computing, 2020,142:36-45.

[1]	徐胜超, 叶力洪. 基于长短期记忆神经网络的容器云队列在线任务动态分配[J]. 计算机与现代化, 2022, 0(07): 79-84.
[2]	狄卫民，赵冬. 废旧家电回收物流网络的选址-定价模型及算法[J]. 计算机与现代化, 2015, 0(5): 71-75+80.
[3]	朱卫宵，祝前旺，陈康. 基于改进蚁群算法的海上编队传感器资源分配模型[J]. 计算机与现代化, 2014, 0(12): 15-18+22.
[4]	任丙忠;孔文焕. 基于统计阈值的Snort规则集动态产生的设计与实现[J]. 计算机与现代化, 2012, 198(2): 165-167.
[5]	鲁胜强;刘瑞玲. 基于最大目标函数的医学图像边缘检测[J]. 计算机与现代化, 2011, 1(6): 5-3.
[6]	孟海战. 一种优化的任务分配方法研究[J]. 计算机与现代化, 2011, 1(4): 135-138.
[7]	王纪伟;高宝成. 一种盲信号分离算法的改进研究 [J]. 计算机与现代化, 2010, 1(02): 52-54.
[8]	郭淑芬;葛云生;刘彩霞. 三层HMIPv6架构下的MAP选择算法[J]. 计算机与现代化, 2009, 1(12): 127-129.

基于双层规划的容器云资源动态配置算法

Dynamic Allocation Algorithm of Container Cloud Resources Based on Bi-level Programming

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