在這個資訊爆炸的時代，解決雲端資料中心的高耗能一直是非常值得探討的議題。動態虛擬機器之整合(Consolidation)，可將資料中心的工作量集中，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，且虛擬機器過度地集中容易讓伺服器出現過載的情況，須透過虛擬機器遷移(Migration)來達到負載平衡。然而，大多數的相關研究僅考慮當下時間的資源使用率，未考慮到未來的資源使用率。因此，本論文運用基於灰色馬可夫的預測模型來預測資料中心未來的資源使用率，以及提出基於預測的演算法，可以達到降低資料中心耗能的目的，減少營運部署之成本，同時也要能夠達到資料中心雲端服務提供者與使用客戶之間的服務層級協議(Service Level Agreement; SLA)以確保要求的服務品質(QoS)。本論文將所提出之方法模擬於CloudSim模擬器且使用
真實世界所測量的虛擬機器負載資料來做模擬，並與其他基準方法做比較，研究結果顯示我們所提出之方法能夠有效降低雲端資料中心的電量與虛擬機器遷移次數，同時確保良好的服務品質。

In the age of data explosion, energy demand of cloud data centers is increasing dramatically, therefore optimizing energy-efficiency is one of the most significant issues of cloud data centers. Dynamic Virtual Machine (VM) consolidation is one of the most promising solutions to reduce energy consumption by concentrating the workload of active hosts and switching idle hosts to sleep mode and improve resource utilization in cloud data centers. However, most of the existing works deal only with minimizing the number of hosts based on their current resource utilization and these works do not explore the future resource requirements. Therefore, unnecessary VM migrations are generated and the rate of Service Level Agreement (SLA) violations are increased in data centers. To address this problem, our VM consolidation approach consists of host overload detection and host underload detection consider both the current and future utilization of resources. The future utilization of resources is accurately predicted using a Grey-Markov based model. In this thesis, We have simulated our algorithms in CloudSim using real world workload traces and compared them with the existing benchmark algorithms. Results show that the proposed approaches significantly reduce the number of VM migration and energy consumption while maintaining the SLA.

參考文獻
[1] A. Ashraf, M. Hartikainen, U. Hassan, K. Heljanko, J. Lilius, T. Mikkonen, I. Porres, M. Syeed, and S. Tarkoma, "Introduction to cloud computing technologies," in Developing Cloud Software: Algorithms, Applications, and Tools, I. Porres, T.Mikkonen, and A. Ashraf, Eds. ^Abo, Finland: Turku Centre for Computer Science (TUCS) General Publication Number 60, Oct. 2013, pp. 141.
[2] A. Ashraf, "Cost-e cient virtual machine management: Provisioning, admission control, and consolidation," Ph.D. dissertation, Turku Centre for Computer Science (TUCS) Dissertations Number 183, ^Abo, Finland, Oct. 2014.
[3] A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya, "A taxonomy and survey of energy-e cient data centers and cloud computing systems," Adv. Comput., vol. 82, pp. 47111, 2011.
[4] A. Beloglazov and R. Buyya, "Managing overload hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints," IEEE Transactions of Parallel and Distributed Systems, 2012.
[5] A. Beloglazov and R. Buyya, "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, pp. 1397-1420, 2012.
[6] P. Barham, B. Dragovic, K. Fraser, S. H, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A.War eld, "Xen and the art of virtualization," in Ninteenth acm Symposium on Operating Systems Principles(SOSP), 2003, pp. 164-177.
[7] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. War eld, "Live migration of virtual machines," in Proc. 2nd Conf. Symp. Netw. Syst. Design Implementation, 2005, vol. 2, pp. 273-286.
[8] R. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, "Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms," Software: Practice and Experience (SPE), vol. 41, pp. 23-50, 2011.
[9] J. L. Deng, "Introduction to Grey system theory", The Journal of Grey System, vol. 1, no. 1, pp. 1-24, 1989.
[10] L. Deboosere, B. Vankeirsbilck, P. Simoens, F. Turck, B. Dhoedt, and P. Demeester, "E cient resource management for virtual desktop cloud computing," J. Supercomput., vol. 62, no. 2, pp. 741-767, 2012.
[11] F. Farahnakian, P. Liljeberg, and J. Plosila, "Lircup: Linear regression based cpu usage prediction algorithm for live migration of virtual machines in data centers," in The 39th Euromicro Conference Series on Software Engineering and Advanced Applications, September 2013.
[12] T. C. Ferreto, M. A. S. Netto, R. N. Calheiros, and C. A. F. De Rose, "Server consolidation with migration control for virtualized data centers," Future Generation Computer System, vol. 27, pp. 1027-1034, 2011.
[13] X. Fu and C. Zhou, "Virtual Machine selection and placement for Dynamic consolidation in cloud computing environment," Frontier of Computer Science, 2015; 9(2):322-330.
[14] A. Greenberg, J. Hamilton, D. A. Maltz, and P. Patel, "The cost of a cloud: research problems in data center networks," ACM SIGCOMM Computer Communication Review, vol. 39, pp. 63-73, 2009.
[15] A. Gandhi, M. Harchol-Balter, R. Raghunathan, and M. A. Kozuch, "AutoScale: Dynamic, robust capacity management for multi-tier data centers," ACM Trans. Comput. Syst., vol. 30, no. 4, pp. 14:1-14:26, 2012.
[16] G. Han, W. Que, G. Jia, L. Shu, "An E cient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing", Sensors, Vol.16, No.2, Article 246, 2016.
[17] S. Islam, J. Keung, K. Lee, and A. Liu, "Empirical prediction models for adaptive resource provisioning in the cloud," Future Generation Computer Systems, vol. 28, pp. 155-162, 2012.
[18] U. Kumar, VK. Jain. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India." Energy 2010;35:1709-16.
[19] W. Li, J. Tordsson, and E. Elmroth, "Modeling for dynamic cloud scheduling via migration of virtual machines," in Third IEEE International Conference on Cloud Computing Technology and Science, 2011.
[20] P. Mell and T.Grance. (2011, Sept.). TheNIST de nition of cloud computing Recommendations of the National Institute of Standards and Technology. Special Publication 800-145 [Online]. Available: http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
[21] C. Mastroianni, M. Meo, and G. Papuzzo, "Probabilistic consolidation of virtual machines in self-organizing cloud data centers," IEEE Transactions on Cloud Computing, vol. 1, pp. 125-228, 2013.
[22] A. Murtazaev and S. Oh, "Sercon: Server consolidation algorithm using live migration of virtual machines for green computing," vol. 28, 2011, pp. 212-231.
[23] Z.-L. Mao and J.-H. Sun, "Application of Grey-Markov model in forecasting fire accidents," Procedia Engineering, vol. 11, pp. 314-318, 2011.
[24] G. Motta, N. Sfondrini, and D. Sacco, "Cloud computing: An architectural and technological overview," in Proc. Int. Joint Conf. Serv. Sci., 2012, pp. 23-27.
[25] R. Nathuji, K. Schwan, "Virtualpower: coordinated power management in virtualized enterprise systems," ACM SIGOPS Operating Systems Review, 41 (6) (2007) 265-278.
[26] K. Park and V. S. Pai, "Comon: A mostly-scalable monitoring system for planetlab," vol. 40, 2006, pp. 65-74.
[27] I. Sriram and A. Khajeh-Hosseini, "Research agenda in cloud technologies," Large Scale Complex IT Syst. (LSCITS), Univ. Bristol, U.K., 2010, http://arxiv.org/ftp/arxiv/papers/1001/1001.3257.pdf
[28] I. Takouna, E. Alzaghoul, and C. Meinel, "Robust virtual machine consolidation for effi cient energy and performance in virtualized data centers" in The IEEE International Conference on Green Computing and Communications, September 2014.
[29] T. Wood, P. Shenoy, A. Venkataramani, and M. Yousif, "Black-box and gray-box strategies for virtual machine migration," in the 4th USENIX conference on Networked systems design and implementation, 2007, pp. 229-242.
[30] Z. Xiao, W. Song, and Q. Chen, "Dynamic resource allocation using virtual machines for cloud computing environment, "IEEE Transactions of Parallel and Distributed Systems, vol. 24, pp. 1107-1116, 2013.