VM migration algorithm for the balance of energy resource across  data centers in cloud computing

doi:10.19682/j.cnki.1005-8885.2019.0022

中国邮电高校学报(英文版) ›› 2019, Vol. 26 ›› Issue (5): 22-32.doi: 10.19682/j.cnki.1005-8885.2019.0022

• Artificial Intelligence • 上一篇下一篇

VM migration algorithm for the balance of energy resource across data centers in cloud computing

Song Da1, Fu Xiong1, Zhou Jingjing2, Wang Junchang1, Zhang Lin1, Deng Song3, Qiao Lei4

1.
2. 南京邮电大学

收稿日期:2018-11-23 修回日期:2019-04-08 出版日期:2019-10-31 发布日期:2019-11-06
通讯作者: 付雄 E-mail:fux@njupt.edu.cn
基金资助:
国家自然科学基金

VM migration algorithm for the balance of energy resource across data centers in cloud computing

Song Da1, Fu Xiong1 , Zhou Jingjing2, Wang Junchang1, Zhang Lin1, Deng Song3, Qiao Lei4

Received:2018-11-23 Revised:2019-04-08 Online:2019-10-31 Published:2019-11-06
Contact: Xiong FU E-mail:fux@njupt.edu.cn
Supported by:
National Natural Science Foundation of China

摘要/Abstract

摘要： Cloud computing makes it possible for users to share computing power. The framework of multiple data centers gains a greater popularity in modern cloud computing. Due to the uncertainty of the requests from users, the loads of CPU(Center Processing Unit) of different data centers differ. High CPU utilization rate of a data center affects the service provided for users, while low CPU utilization rate of a data center causes high energy consumption. Therefore, it is important to balance the CPU resource across data centers in modern cloud computing framework. A virtual machine (VM)migration algorithm was proposed to balance the CPU resource across data centers. The simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.

关键词: cloud computing, load balancing, across data centers, virtual machine migration

Abstract: Cloud computing makes it possible for users to share computing power. The framework of multiple data centers gains a greater popularity in modern cloud computing. Due to the uncertainty of the requests from users, the loads of CPU(Center Processing Unit) of different data centers differ. High CPU utilization rate of a data center affects the service provided for users, while low CPU utilization rate of a data center causes high energy consumption. Therefore, it is important to balance the CPU resource across data centers in modern cloud computing framework. A virtual machine(VM) migration algorithm was proposed to balance the CPU resource across data centers. The simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.

Key words: cloud computing, load balancing, across data centers, virtual machine migration

Song Da, Fu Xiong, Zhou Jingjing, Wang Junchang, Zhang Lin, Deng Song, Qiao Lei. VM migration algorithm for the balance of energy resource across data centers in cloud computing[J]. The Journal of China Universities of Posts and Telecommunications, 2019, 26(5): 22-32.

参考文献

1. Mark S, David S, Frederic V, et al. Resource allocation algorithms for virtualized service hosting platforms. Journal of Parallel and Distributed Computing, 2010, 70(9): 962-974 2. Adnan A, Keijo H, Johan L, et al. Introduction to cloud computing technologies in Developing Cloud Software: Algorithms, Applications, and Tools, Finland: Turku Centre for Computer Science (TUCS) General Publication, 2013, 60: 1–41 3. Michael C, Aameek S, Himabindu P, et al. Exploiting Spatio-Temporal Tradeoffs for Energy-Aware MapReduce in the Cloud. Proceedings of the 4th International Conference on Cloud Computing, 2011: 251-258. 4. Ching C T M, Dusit N, Tham C K. Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing. Proceedings of the IEEE International Conference on Advanced Information Networking and Applications, IEEE Computer Society, 2011:348-355 5. Chen M, Zhang H, Su Y Y, et al. Effective VM sizing in virtualized data centers. Proceedings of the IEEE International Symposium on Integrated Network Management, May 23-27, Dublin, Ireland, 2011: 594 - 601 6. Rimal B P, Jukan A, Katsaros D, et al. Architectural requirements for cloud computing systems: An enterprise cloud approach. Journal of Grid Computing, 2011, 9(1): 3–26 7. Paya A, Dan C M. Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem. IEEE Transactions on Cloud Computing, 2017, 5(1):15-27 8. Mainak A, Tarachand A. Heuristic-based load-balancing algorithm for IaaS cloud. Future Generation Computer Systems, 2018, 81(2018): 156-165 9. Altaf H, Muhammad A, Abid K et al. RALBA: a computation-aware load balancing scheduler for cloud computing. Cluster Computing, 2018, 21(3): 1667-1680. 10. Fu X, Chen J, Deng S, et al. Layered virtual machine migration algorithm for network resource balancing in cloud computing. Frontiers of Computer Science, 2018, 12(1): 75-85. 11. Fu X, Zhou C. Virtual machine selection and placement for dynamic consolidation in cloud computing environment. Frontiers of Computer Science, 2015, 9(2): 322-330 12. Yang X, Zhang H, Ma H, et al. Multi-resource allocation for virtual machine placement in video surveillance cloud. Proceedings of the International Conference on Human Centered Computing, 2016: 544-555 13. Li X, Qian Z, Lu S, Wu J. Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Mathematical & Computer Modelling, 2013, 58(5-6): 1222-1235 14. Hu Z, Li B, Luo J. Flutter:Scheduling tasks closer to data across geo-distributed datacenters. Proceedings of the 35th Annual IEEE International Conference on Computer Communications, April 10-14, San Francisco, CA, 2016:1-9. 15. Cao Z, Dong S. Dynamic VM consolidation for energy-aware and SLA violation reduction in cloud computing. Proceedings of the 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies, 2012:363-369 16. Cui Y, Yang Z, et al. Traffic-aware virtual machine migration in topology-adaptive DCN. Proceedings of the 24th International Conference on Network Protocols (ICNP), Singapore, 2016: 1-10 17. Baccarelli E, Amendola D, Cordeschi N. Minimum-energy bandwidth management for QoS live migration of virtual machines. Computer Networks, 2015, 93(P1):1-22 18. Cordeschi N, Shojafar M, Amendola D, et al. Energy-e?cient adaptive networked datacenters for the QoS support of real-time applications. Journal of Supercomputing, 2015, 71(2):448–478 19. 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. Software: Practice and Experience, 2011, 41(1): 23–50 20. Beloglazov A, Buyya R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance e?cient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 2012, 24(13): 1397–1420

VM migration algorithm for the balance of energy resource across data centers in cloud computing

VM migration algorithm for the balance of energy resource across data centers in cloud computing

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