資料中心的高耗能一直以來都是大家討論的議題，動態虛擬機整合，可將資料中心的工作集中，讓低使用率的伺服器休眠，有效降低資料中心的耗能，達到節省能源與環境保護的效果，虛擬機集中容易讓伺服器出現過載的情況，違反了雲端服務業者與使用者所簽訂的服務品質(QoS)，須透過虛擬機搬移來達到負載平衡，因此主要的挑戰為如何減少資料中心的耗能同時符合要求的QoS。 虛擬機整合為NP-hard 問題，本研究提出利用虛擬機的歷史訊息算出標準差，預測其往後的資源使用量震盪幅度，配置在最佳的伺服器中，在伺服器過載時適當的挑選虛擬機，搬移至適當的伺服器中，減輕服務協議的違反量，將最低負載的伺服器裡的虛擬機搬移出去，使其休眠，降低資料中心的耗能，目標為讓各台伺服器在不違反服務協定的條件下，盡可能的使用伺服器的資源，達到負載平衡的效果，本研究模擬實驗結果比較相關之研究方法，可大幅減少虛擬機搬移次數，並在不增加耗能的情況下，減低服務協議違反量的發生，進行負載平衡所花費的時間也較比較對象減少許多。

High energy consumption of data centers is an important issue which is widely discussed. Dynamic consolidation of Virtual Machine (VM) can reduce energy consumption by concentrating the workload of active hosts and switching idle hosts to sleep mode. However, VM migrations would cause some cost and consolidation might bring more resource demands and lead to violate service level agreements (SLA) between cloud computing providers and users. The main challenges in this study are to reduce the energy consumption of datacenters while meeting their quality of service (QoS) requirements. The goal of this study is to achieve balance between resource utilization and SLA violation of the datacenter. We proposed a VM allocation mechanism that reduce the number of migrations and the energy consumption while not causing SLA violations. By using the requirement history of VMs to calculate standard deviations (STD), the mechanism can predict future resource requirements of each VM and reallocate VMs into the server under the QoS requirements. When some servers overload in a datacenter, suitable VMs are selected from these servers and migrated to appropriate servers to meet the requirement of SLA. On the other hand, when some servers underload, all VMs are migrated out from underload server, switched to sleep mode. Compared to the heuristics proposed in previous studies, the proposed method could greatly decrease number of migrations、SLA violations and execution time.

[1] Amazon EC2. Available from: http://aws.amazon.com/ec2/.

[2] E. Arianyan, H. Taheri, and S. Sharifian. “Novel heuristics for consolidation of virtual machines in cloud data centers using multi-criteria resource management solutions,” The Journal of Supercomputing, vol. 72, no. 2, pp. 688-717, 2016.

[3] L. A. Barroso, and U. Holzle. “The Case for Energy-Proportional Computing,” Computer, vol. 40, no. 12, pp. 33-37, 2007.

[4] A. Beloglazov, J. Abawajy, and R. Buyya. “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing,” Future Generation Computer Systems, vol. 28, no. 5, pp. 755-768, 2012.

[5] A. Beloglazov, and R. Buyya. “Adaptive Threshold-Based Approach for Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers,” Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science, pp.1-6, 2010.

[6] A. Beloglazov and R. Buyya. “Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers,” Concurrency and Computation: Practice and Experience, vol. 24, no. 13, pp. 1397-1420, 2012.

[7] A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya. “A Taxonomy and Survey of energy-efficient data centers and Cloud Computing systems,” Advances in Computers, vol. 82, pp. 47-111, 2011.

[8] R. N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F.D. Rose, and R. Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software—Practice & Experience, vol. 41, no. 1, pp. 23-50, 2010.

[9] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield. “Live migration of virtual machines,” Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation, vol. 2, pp. 273-286, 2005.

[10] T. V. T. Duy, Y. Sato, and Y. Inoguchi. “Performance evaluation of a Green Scheduling Algorithm for energy savings in Cloud computing,” Parallel & Distributed Processing, Workshops and Phd Forum, IEEE International Symposium on, pp. 1-8, 2010.

[11] F. Farahnakian, T. Pahikkala, P. Liljeberg, and J. Plosila. “Energy aware consolidation algorithm based on K-nearest neighbor regression for cloud data centers,” Utility and Cloud Computing (UCC), 6th IEEE/ACM Internatonal Conference on, pp. 256-259, 2013.

[12] F. Farahnakian, T. Pahikkala, P. Liljeberg, J. Plosila, and H. Tenhunen. “Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing,” IEEE 8th International Conference on Cloud Computing, pp. 371-388, 2015.

[13] A. Gandhi, M. Harchol-Balter, R. Das, C. Lefurgy. “Optimal power allocation in server farms,” ACM SIGMETRICS Performance Evaluation Review, vol. 37, no. 1, pp. 157–168, 2009.

[14] J. Glanz. “Power, pollution and the internet.” The New York Times, 2012.

[15] H. P. Jiang, M. L. Weng, and W. M. Chen. “Dynamic Consolidation of Virtual
Machines in Cloud Datacenters,” IEICE Transactions on Information and
Systems, vol. E97-D, no. 7, pp. 1727-1730, 2014.

[16] A. Kivity, Y. Kamay, D. Laor, U. Lublin, and A. Liguori. “kvm: the Linux Virtual Machine Monitor,” Proceedings of the Linux Symposium, pp. 225–230, 2007.

[17] T. Kuroda, K. Suzuki, S. Mita, T. Fujita, F. Yamane, F. Sano, A. Chiba, Y. Watanabe, K. Matsuda, T. Maeda, T. Sakurai, and T. Furuyama. “Variable supply-voltage scheme for low-power high-speed CMOS digital design,” IEEE Journal of Solid-State Circuits, vol. 33, no. 3, pp. 454-462, 1998.

[18] D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang. “Power and performance management of virtualized computing environments via lookahead control,” Cluster Computing, vol. 12, no. 1, pp. 1–15, 2009.

[19] H. Lin, X. Qi, S. Yang, and S. Midkiff. “Workload-Driven VM Consolidation in
Cloud Data Centers,” Parallel and Distributed Processing Symposium (IPDPS), IEEE International, pp. 207-216, 2015.

[20] P. Mell, and T. Grance. The NIST Definition of Cloud Computing,” National Institute of Standards & Technology, 2011

[21] D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, D. Zagorodnov. “The Eucalyptus Open-source Cloud-computing System,” Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid. pp.124-131, 2009.

[22] K. Park and V.S. Pai. “CoMon: A Mostly-Scalable Monitoring System for
PlanetLab,” ACM SIGOPS Operating Systems Review, vol. 40, no. 1, pp. 65-74, 2006.

[23] C. Reiss, J. Wilkes, and J. L. Hellerstein. “Google cluster-usage traces: format + schema,” Google Inc., White Paper, pp. 1-14, 2011.

[24] M. Rosenblum, V. Inc, and T. Garfinkel. “Virtual Machine Monitors: Current
Technology and Future Trends,” Computer, vol. 38, no. 5, pp. 39-47, 2005.

[25] S. Son, G. Jung, and S. C. Jun. “An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider,” The Journal of Supercomputing, vol. 64, no. 2, pp. 606-637, 2013.

[26] B. Tomlinson, M. Silberman, and J. White. “Can More Efficient IT Be Worse for the Environment,” Computer, vol. 44, no. 1, pp. 87-89, 2011.

[27] W. Voorsluys, J. Broberg, S. Venugopal, and R. Buyya. “Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation,” Proceedings of the 1st International Conference on Cloud Computing, pp. 254 – 265, 2009.

[28] H. Yu, D. Zheng, B. Y. Zhao, and W. Zheng. “Understanding user behavior in large-scale video-on-demand systems,” Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems, vol. 40, no. 4, pp. 333-344, 2006.

[29] Z. Zhou, Z. Hu, and K. Li. “Virtual Machine Placement Algorithm for Both Energy-Awareness and SLA Violation Reduction in Cloud Data Centers,” Scientific Programming, vol. 2016, Article ID 5612039, 11 pages, 2016.