Greedy algorithm-based virtual machine migration strategies in cloud data center

doi:10.11772/j.issn.1001-9081.2019040598

Abstract

Abstract: In order to save the energy consumption in cloud data center, some greedy algorithms-based Virtual Machine (VM) migration strategies were proposed. In these strategies, the virtual migration process was divided into physical host status detection, virtual machine selection and virtual machine placement, and the greedy algorithm was adopted in the process of virtual selection and virtual placement respectively. The three proposed migration strategies were:Minimum Host Utilization selection, Maximum Host Utilization placement (MinMax_Host_Utilization); Maximum Host Power Usage selection, Minimum Host Power Usage placement (MaxMin_Host_Power_Usage); Minimum Host MIPS selection, Maximum Host MIPS placement (MinMax_Host_MIPS). The maximum or minimum thresholds were set for the processor utilization efficency, the energy consumption and the processor computing power of physical host. According to the principle of greedy algorithm, the virtual machines with indicators higher or lower than the thresholds should be migrated. With CloudSim as the simulated cloud data center, the test results show that compared with the static threshold and median absolute deviation migration strategies existing in CloudSim, the proposed strategies have the total energy consumption reduced by 15%, the virtual machine migration number decreased by 60%, and the average SLA violation rate lowered about 5%.

Key words: low energy consumption, Service Level Agreement (SLA) violation, virtual machine migration, cloud data center, greedy algorithm

摘要： 为了节省云数据中心的能量消耗，提出了几种基于贪心算法的虚拟机（VM）迁移策略。这些策略将虚拟机迁移过程划分为物理主机状态检测、虚拟机选择和虚拟机放置三个步骤，并分别在虚拟机选择和虚拟机放置步骤中采用贪心算法予以优化。提出的三种迁移策略分别为：最小主机使用效率选择且最大主机使用效率放置算法MinMax_Host_Utilization、最大主机能量使用选择且最小主机能量使用放置算法MaxMin_Host_Power_Usage、最小主机计算能力选择且最大主机计算能力放置算法MinMax_Host_MIPS。针对物理主机处理器使用效率、物理主机能量消耗、物理主机处理器计算能力等指标设置最高或者最低的阈值，参考贪心算法的原理，在指标上超过或者低于这些阈值范围的虚拟机都将进行迁移。利用CloudSim作为云数据中心仿真环境的测试结果表明，基于贪心算法的迁移策略与CloudSim中已存在的静态阈值迁移策略和绝对中位差迁移策略比较起来，总体能量消耗少15%，虚拟机迁移次数少60%，平均SLA违规率低5%。

关键词: 低能量消耗, 服务等级协议违规, 虚拟机迁移, 云数据中心, 贪心算法

CLC Number:

TP393.093

LIU Kainan. Greedy algorithm-based virtual machine migration strategies in cloud data center[J]. Journal of Computer Applications, 2019, 39(11): 3333-3338.

刘开南. 云数据中心基于贪心算法的虚拟机迁移策略[J]. 计算机应用, 2019, 39(11): 3333-3338.

References

[1] ARIANYAN E,TAHERI H,KHOSHDEL V. Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers[J]. Journal of Network & Computer Applications, 2017, 78:43-61.
[2] LEI Z, SUN E, CHEN S, et al. A novel hybrid-copy algorithm for live migration of virtual machine[J]. Future Internet, 2017, 9(3):1-13.
[3] JIN X, ZHANG F, WANG L, et al. Joint optimization of operational cost and performance interference in cloud data centers[J]. IEEE Transactions on Cloud Computing, 2017, 5(4):697-711.
[4] 张玉清,王晓菲,刘雪峰,等. 云计算环境安全综述[J]. 软件学报, 2016, 27(6):1328-1348. (ZHANG Y Q, WANG X F, LIU X F, et al. Survey on cloud computing security[J]. Journal of Software, 2016, 27(6):1328-1348.)
[5] 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 & Experience, 2011, 41(1):23-50.
[6] BELOGLAZOV A, BUYYA R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers[J]. Concurrency and Computation Practice and Experience, 2012, 24(13):1397-1420.
[7] CloudsLab. CloudSim:a framework for modeling and simulation of cloud computing infrastructures and services[EB/OL].[2019-01-03]. http://github.com/Cloudslab/cloudsim.
[8] BELOGLAZOV A, ABAWAJY J, BUYYA R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing[J]. Future Generation Computer Systems, 2012, 28(5):755-768.
[9] 叶可江,吴朝晖,姜晓红,等. 虚拟化云计算平台的能耗管理[J]. 计算机学报, 2012, 35(6):1262-1285. (YE K J, WU Z H, JIANG X H, et al. Power management of virtualized cloud computing platform[J]. Chinese Journal of Computers, 2012, 35(6):1262-1285.)
[10] CHASE J, NIYATO D. Joint optimization of resource provisioning in cloud computing[J]. IEEE Transactions on Services Computing, 2017, 10(3):396-409.
[11] ZHOU B, WU J, WANG L, et al. Joint optimization of server and network resource utilization in cloud data centers[C]//Proceedings of the 2018 IEEE Global Communications Conference. Piscataway:IEEE, 2018:1-6.
[12] LI Y, LIU J, CAO B, et al. Joint optimization of radio and virtual machine resources with uncertain user demands in mobile cloud computing[J]. IEEE Transactions on Multimedia, 2018, 20(9):2427-2438.
[13] GARG S K, YEO C S, BUYYA R. Green cloud framework for improving carbon efficiency of clouds[C]//Proceedings of the 2011 European Conference on Parallel Processing, LNCS 6852. Berlin:Springer, 2011:491-502.
[14] SALEHI M A, KRISHNA P R, DEEPAK K S, et al. Preemption-aware energy management in virtualized data centers[C]//Proceedings of the IEEE 5th International Conference on Cloud Computing. Piscataway:IEEE, 2012:844-851.
[15] CHOUDHARY M, PEDDOJU S K. A dynamic optimization algorithm for task scheduling in cloud environment[J]. International Journal of Engineering Research & Applications, 2012, 2(3):2564-2568.
[16] SHARMA R K, SHARMA N. A dynamic optimization algorithm for task scheduling in cloud computing with resource utilization[J]. International Journal of Scientific Engineering & Technology, 2013, 2(10):1062-1068.
[17] PAUL A K, ADDYA S K, SAHOO B, et al. Application of greedy algorithms to virtual machine distribution across data centers[C]//Proceedings of the 2014 IEEE India Conference. Piscataway:IEEE, 2014:1-6.
[18] PORTALURI G, GIORDANO S, KLIAZOVICH D, et al. A power efficient genetic algorithm for resource allocation in cloud computing data centers[C]//Proceedings of the IEEE 3rd International Conference on Cloud Networking. Piscataway:IEEE, 2014:58-63.
[19] WANG J V, CHENG C T, TSE C K. A power and thermal-aware virtual machine allocation mechanism for cloud data centers[C]//Proceedings of the 2015 IEEE International Conference on Communication Workshop. Piscataway:IEEE, 2015:2850-2855.
[20] AHAMED F, SHAHRESTANI S, JAVADI B. Security aware and energy-efficient virtual machine consolidation in cloud computing systems[C]//Proceedings of the 2016 IEEE Trustcom/BigDataSE/ISPA. Piscataway:IEEE, 2016:1516-1523.
[21] VASUDEVAN M, TIAN Y, TANG M, et al. Energy-efficient application assignment in profile-based data center management through a repairing genetic algorithm[J]. Applied Soft Computing, 2018, 67:399-408.
[22] BEIK R. Green cloud computing:greedy algorithms for virtual machines migration and consolidation to optimize energy consumption in a data center[J]. International Journal of Digital Application & Contemporary Research, 2014, 2(9):1-9.
[23] MAURYA K, SINHA R. Energy conscious dynamic provisioning of virtual machines using adaptive migration thresholds in cloud data center[J]. International Journal of Computer Science & Mobile Computing, 2013, 2(3):74-82.
[24] MANN Z A. Interplay of virtual machine selection and virtual machine placement[C]//Proceedings of the 2016 European Conference on Service-Oriented & Cloud Computing, LNCS 9846. Berlin:Springer, 2016:137-151.
[25] JOSEPH C T, MARTIN J P. Task Dependency Aware Selection (TDAS) in cloud[J]. Procedia Computer Science, 2016, 93:269-275.
[26] WANG J V, FOK K Y, CHENG C T, et al. A stable matching-based virtual machine allocation mechanism for cloud data centers[C]//Proceedings of the 2016 IEEE World Congress on Services. Piscataway:IEEE, 2016:103-106.