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Improvement of Energy Efficiency at Cloud Data Center Based on Fuzzy Markov Normal Algorithm VM Selection in Dynamic VM Consolidation

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The problem of the energy consumption in cloud data center has become an important study in recent years. Dynamic VM consolidation has been known as a solution able to reduce energy consumption in cloud data center. However, there are many problems in Dynamic VM consolidation, in particular the VM selection. The objective of VM selection is to choose VM candidates suitable to move from overload host for avoiding oversubscribed host and to give an impact for the reduction of energy consumption. This research proposes a VM selection model in dynamic VM consolidation to improve the energy efficiency in cloud data center based on Fuzzy Markov Normal Algorithm. Fuzzy logic has been used for categorizing the attributes of VM candidates. After that, Markov Normal Algorithm has been used for deciding to which category VM should be migrated from overload host. The proposed VM selection model has been evaluated using various VM instance conditions (homogeneous or heterogeneous) with datasets from PlanetLabs in Cloudsim. Moreover, several parameters were used to measure the performance in this research such as Energy Consumption, SLA Violation, SLA Time per active host, and Performance Degradation Due Migration. The results experiments have shown the proposed VM selection model capable of improving energy efficiency in cloud data center up to 3.74%, 6.65%, 5.36%, and 5.11%, compared with the existing VM selections such as Constant First Selection, Minimum Migration Time, Random Choice, and Maximum Correlation.
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Cloud Data Center; Energy Aware; Dynamic VM Consolidation; VM Selection; Fuzzy Logic; Markov Normal Algorithm

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A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,” Futur. Gener. Comput. Syst., vol. 28, no. 5, pp. 755–768, 2012.

R. Brown and others, “Report to congress on server and data center energy efficiency: Public law 109-431,” 2008.

A. Kulseitova and A. T. Fong, “A survey of energy-efficient techniques in cloud data centers,” Proc. - Int. Conf. ICT Smart Soc. 2013 "Think Ecosyst. Act Converg. ICISS 2013, pp. 267–271, 2013.

A. Beloglazov and R. Buyya, “Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers,” in Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science, 2010, no. December 2010, p. 4.

G. Keller, M. Tighe, H. Lutfiyya, and M. Bauer, “An analysis of first fit heuristics for the virtual machine relocation problem,” in Network and Service Management (CNSM), 2012 8th International Conference on, 2012, pp. 406–413.

A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya, “Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers,” Concurr. Comput. Pract. Exp., vol. 2, no. 13, pp. 1397–1420, 2012.

A. Beloglazov and R. Buyya, “Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints,” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 7, pp. 1366–1379, Jul. 2013.

Zhihong Li, W. Luo, X. Lu, and J. Wei, “A Live Migration Strategy for Virtual Machine Based on Performance Predicting,” in 2012 International Conference on Computer Science and Service System, 2012, pp. 72–76.

S. Masoumzadeh and H. Hlavacs, “Integrating VM Selection Criteria in Distributed Dynamic VM Consolidation Using Fuzzy Q-Learning,” in 9th International Conference on Netork and Service Management (CNSM), 2013, pp. 332–338.

L. Hongyou and W. Jiangyong, “Energy-aware scheduling scheme using workload-aware consolidation technique in cloud data centres,” Commun. China, vol. 10, no. 12, pp. 114–124, 2013.

W. Shu, W. Wang, and Y. Wang, “A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing,” EURASIP J. Wirel. Commun. Netw., vol. 64, no. 1, 2014.

X. Fu and C. Zhou, “Virtual machine selection and placement for dynamic consolidation in Cloud computing environment,” Front. Comput. Sci., vol. 9, no. 2, pp. 322–330, 2015.

Z. Zhou, Z. Hu, T. Song, and J. Yu, “A novel virtual machine deployment algorithm with energy efficiency in cloud computing,” J. Cent. South Univ., vol. 22, no. 3, pp. 94–983, 2015.

G. F. Shidik and A. Ashari, “Efficiency Energy Consumption in Cloud Computing Based on Constant Position Selection Policy in Dynamic Virtual Machine Consolidation,” Adv. Sci. Lett., vol. 20, no. 10–11, pp. 2119–2124, 2014.

G. F. Shidik, Azhari, and K. Mustofa, “Evaluation of Selection Policy with Various Virtual Machine Instances in Dynamic VM Consolidation for Energy Efficient at Cloud Data Centers,” J. Networks, vol. 10, no. 7, pp. 397–406, 2015.

L. A. Zadeh, Fuzzy Sets, Fuzzy Logic, Fuzzy Systems. World Scientific, 1996.

J. Li and W. Fan, “Coordination Scheduling Based On Fuzzy Concepts,” in First International Conference on Machine Learning and Cybernatics, 2002, no. November, pp. 1489–1492.

J. Xu, M. Zhao, J. Fortes, R. Carpenter, and M. Yousif, “On the Use of Fuzzy Modeling in Virtualized Data Center Management,” Fourth Int. Conf. Auton. Comput., pp. 25–25, Jun. 2007.

J. Xu, M. Zhao, J. Fortes, R. Carpenter, and M. Yousif, “Autonomic resource management in virtualized data centers using fuzzy logic-based approaches,” Cluster Comput., vol. 11, no. 3, pp. 213–227, 2008.

J. Xu and J. a. B. Fortes, “Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments,” 2010 IEEE/ACM Int. Conf. Green Comput. Commun. Int. Conf. Cyber, Phys. Soc. Comput., pp. 179–188, Dec. 2010.

K. Mukherjee and G. Sahoo, “Mathematical Model of Cloud Computing Framework Using Fuzzy Bee Colony Optimization Technique,” in 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009, pp. 664–668.

M. Sithu and N. L. Thein, “A Resource Provisioning Model for Virtual Machine Controller Based on Neuro-Fuzzy System,” in The 2nd International Conference on Next Generation Information Technology (ICNIT), 2011, pp. 109–114.

F. Ramezani, J. Lu, and F. Hussain, “An online fuzzy Decision Support System for Resource Management in cloud environments,” 2013 IFSA World Congr. NAFIPS Annu. Meet., pp. 754–759, Jun. 2013.

L. Gong, J. Xie, X. Li, and B. Deng, “Study on energy saving strategy and evaluation method of green cloud computing system,” in Industrial Electronics and Applications (ICIEA), 2013, pp. 483–488.

J. Katzenelson, “The Markov algorithm as a language parser—Linear bounds,” J. Comput. Syst. Sci., vol. 6, no. 5, pp. 465–478, Oct. 1972.

G.F. Shidik, R. Pulungan R, Application of Markov’s Normal Algorithm, Advanced Science Letters, vol 21, no 10, pp.3271–3274, 2015.

N. A. Shanin, “Constructive real numbers and constructive function spaces,” in American Mathematical Society, 1968.

E. G. Rajan, “Symbolic Computing - Signal and Image Processing,” Anshan Publ., 2005.

M. R. Chowdhury, M. R. Mahmud, and R. M. Rahman, “Study and performance analysis of various VM placement strategies,” J. Cloud Comput. Adv. Syst. Appl., vol. 4, no. 20, pp. 1–21, 2015.

D. Feitelson, Workload modeling for computer systems performance evaluation. Cambrifge University Press, 2015.

R. N. 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,” Softw. Pract. Exp. Wiley Press. New York, USA, vol. 41, no. 1, pp. 23–50, 2011., “SPECpower_ssj2008 Hewlett-Packard Company ProLiant ML110 G5,” 2008. [Online]. Available:


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