Server consolidation has recently become a major issue in cloud data centers. Energy efficiency is a key aspect in solving server consolidation issues. Virtual machine placement algorithms play an important role in maximizing the resource utilization may lead to energy efficiency. Constraint programming is an approach used to find the available servers and migrate these servers to the virtual machines by treating them as constraints to find optimal solution. The objective function of this work is to increase resource utilization by minimizing the number of active physical machines and decrease the search time of constraint solver for better energy efficiency. In this paper a Best Fit Resource Utilization ( BFRU) algorithm is proposed for virtual machine placement, it use best fit algorithm technique. However, virtual machine placement problem is formulated as a variant of multidimensional bin packing problem and then a constraint solver is used to solve the problem using the dataset collected from Amazon EC2.The proposed BFRU algorithm implemented through NetBeans IDE to evaluate the data collected. The simulation result shows that constraint programming based virtual machine placement algorithm BFRU can effectively reduce the energy consumption with respect to memory.

BFRU algorithm, Amazon EC2, Best Fit Resource Utilization ( BFRU) algorithm

[1].Mustafa,K.Bilal,A.Hayat,A.R.Khan,S.A.Madani,”Resource Management in cloud computing: Taxonomy, Prospects and challenges”, Computers and Electrical Engineering,2015,DOI:10.1016/j.compeleceng.2015.07.021
[2] G.Xiao-ming,H.Jie and C.Long,”principle and implementation of virtualization technology”,Electronic Industry Press,Beijing,(2012).
[3] H.Jin,D.Pan,J.Xu and N.Pissinou,”Efficient VM Placement with multiple deterministic and Stochastic resources in datacenters,” in IEEE Global Communications Conference(GLOBECOM),2012,pp.2505-2510.
[4] A.Gupta,L.V.Kale,D.Milojicic,P.Faraboschi,and S.M.Balle,”HPC-aware VM placement in infrastructure clouds”, in IEEE international conference on cloud Engineering,2013,pp.11-20.
[5] X.Li,Z.Qian,R.Chi,B.Zhang and S.Lu,”Balancing resource utilization for continous virtual machine requests in clouds”,in 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing,2012,pp.266-273.
[6] Koomey J.Growth in datacenter electricity use 2005 to 2010.A report by Analytical Press completed the request of ‘The New York Times’,2011.
[7] Cai C,Wang L,Khan S U,et al,”Energy-aware high performance computing:A Taxonomy study,”Parallel and distributed systems(ICPADS),,2011 IEEE 17th International Conference on IEEE,2011:953-958.
[8] Pocket gems on google cloud platform,http://cloud.google.com/customers/pocketgems.
[9] C.Pandiselvi, Dr.S.Sivakumar,” A Review of Virtual machine placement algorithm in cloud datacenters for server consolidation”, International journal of engineering Research in Computer Science and Engineering(IJERCSE),Vol 5,Issue 3,March 2018,pp(182-188).
[10] Verma, A., Ahuja, P., Neogi, A.: pmapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, pp. 243–264. Springer-Verlag New York, Inc. (2008)
[11] Wood, T., Shenoy, P., Venkataramani, A., Yousif, M.: Sandpiper: Black-box and gray-box resource management for virtual machines. Comput. Netw. 53(17), 2923– 2938 (2009)
[12] Kumar, S., Talwar, V., Kumar, V., Ranganathan, P., Schwan, K.: vmanage: loosely coupled platform and virtualization management in data centers. In: Proceedings of the 6th International Conference on Autonomic Computing, pp. 127–136. ACM (2009).
[13] Dhiman, G., Marchetti, G., Rosing, T.: vgreen: A system for energy-efficient management of virtual machines. ACM Transactions on Design Automation of Electronic Systems (TODAES) 16(1), 6 (2010)
[14] Rossi, F., Van Beek, P., Walsh, T.: Handbook of constraint programming, vol. 35. Elsevier Science (2006)
[15] Campegiani, P.: A genetic algorithm to solve the virtual machines resources allocation problem in multi-tier distributed systems. In: Second International Workshop on Virtualization Performance: Analysis, Characterization, and Tools (VPACT 2009), Boston, Massachusett (2009)
[16] Xu, J., Fortes, J.: Multi-objective virtual machine placement in virtualized data center environments. In: 2010 EEE/ACM Int’l Conference on & Int’l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom), pp. 179–188. IEEE (2010)
[17] Gao Y,Guan H,Qi Z,et al. “A multi objective ant colony system algorithm for virtual machine placement in cloud computing, ”Journal of Computer and System Sciences,2013,79(8),pp 1230-1242
[18]Minas L, Ellison B. “Energy Efficiency for Information Technology:How to Reduce Power Consumption in Servers and Data Centers. Intel Press, 2009.
[19] Kusic D, Kephart J O, Hanson J E, et al. “Power and performance management of virtualized computing environments via lookahead control,” Cluster computing, 2009, 12(1): 1-15.
[20] Rivoire S, Ranganathan P, Kozyrakis C. “A Comparison of High-Level Full-System Power Models,” HotPower, 2008, 8, pp.3-3.
[21] Lee Y C, Zomaya A Y. “Energy conscious scheduling for distributed computing systems under different operating conditions,” IEEE Transactions on Parallel and Distributed Systems, 2011, 22(8), pp.1374- 1381.
[22] Amazon EC2,http://aws.amazon.com/ec2/,2013.