Energy Efficient Virtual Machine Migration Algorithm
Abstract
Today, a large volume of computing and data storage is done using cloud computing. Therefore, efficient datacenter resource utilization and energy consumption are considered important issues. Virtualization makes effective use of the datacenter’s hardware resources by using Virtual Machines (VM). VM(s) can function completely as a discrete unit and share the underlying hardware resources. The ability to migrate VMs within datacenter servers can considerably enhance the datacenter’s performance and resource utilization. However, most of the existing methods do not consider reducing energy consumption while migrating VM(s).
In this paper, we present a novel Energy Efficient Virtual Machine Migration (EVM) technique that considers various vital factors of the datacenter servers while migrating VMs. Our proposed EVM technique is based on Energy based Server Selection (ESS) approach, which uses Highest Energy First (HEF) strategy for choosing the victim server to be switched off. In addition, ESS uses Lowest Energy First (LEF) strategy for target server selection to host the migrated VMs. The algorithm attempts to achieve efficient energy consumption at the datacenter by switching off underutilized servers. Our comparative evaluation results show that the proposed algorithm has lower overhead in terms of lower number of server state changes, VM migrations and oscillations, and yet outperforms existing methods. At 30% server load, the energy savings achieved using EVM is 31% more than Arbitrary Server Selection (ASS) and 15% more than First Fit Strategy (FFS). Moreover, EVM offers significant reduction in carbon footprint of 20% higher than ASS and 10% higher than FFS.
References
Akoush, S., Sohan, R., Rice, A., Moore, A.W. and Hopper, A., 2010, August. Predicting the performance of virtual machine migration. In Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2010 IEEE International Symposium on (pp. 37-46). IEEE.
Al Shayeji, M.H. and Samrajesh, M.D., 2012, August. An energy-aware virtual machine migration algorithm. In Advances in Computing and Communications (ICACC), 2012 International Conference on (pp. 242-246). IEEE.
Alahmadi, A., Alnowiser, A., Zhu, M.M., Che, D. and Ghodous, P., 2014, March. Enhanced first-fit decreasing algorithm for energy-aware job scheduling in cloud. In Computational Science and Computational Intelligence (CSCI), 2014 International Conference on (Vol. 2, pp. 69-74). IEEE.
Ardagna, D., Panicucci, B., Trubian, M. and Zhang, L., 2012. Energy-aware autonomic resource allocation in multitier virtualized environments. Services Computing, IEEE Transactions on, 5(1), pp.2-19.
Arzuaga, E. and Kaeli, D.R., 2010, January. Quantifying load imbalance on virtualized enterprise servers. In Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering (pp. 235-242). ACM..
Barroso, L.A. and Hölzle, U., 2007. The case for energy-proportional computing. Computer, (12), pp.33-37.
Beloglazov, A. and Buyya, R., 2010, May. Energy efficient allocation of virtual machines in cloud data centers. In Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on (pp. 577-578). IEEE.
Box, G.E. and Muller, M.E., 1958. A note on the generation of random normal deviates. The annals of mathematical statistics, 29(2), pp.610-611.
Buyya, R., Beloglazov, A. and Abawajy, J., 2010. Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308.
Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L. and Zhao, F., 2008, April. Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services. In NSDI (Vol. 8, pp. 337-350)
Chen, M., Zhang, H., Su, Y.Y., Wang, X., Jiang, G. and Yoshihira, K., 2011, May. Effective VM sizing in virtualized data centers. In Integrated Network Management (IM), 2011 IFIP/IEEE International Symposium on (pp. 594-601). IEEE.
Clark, C., Fraser, K., Hand, S., Hansen, J.G., Jul, E., Limpach, C., Pratt, I. and Warfield, A., 2005, May. Live migration of virtual machines. InProceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation-Volume 2 (pp. 273-286). USENIX Association.
Clark, C., Fraser, K., Hand, S., Hansen, J.G., Jul, E., Limpach, C., Pratt, I. and Warfield, A., 2005, May. Live migration of virtual machines. InProceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation-Volume 2 (pp. 273-286). USENIX Association.
Engelmann, C., Vallee, G.R., Naughton, T. and Scott, S.L., 2009, February. Proactive fault tolerance using preemptive migration. In Parallel, Distributed and Network-based Processing, 2009 17th Euromicro International Conference on (pp. 252-257). IEEE.
Fan, X., Weber, W.D. and Barroso, L.A., 2007, June. Power provisioning for a warehouse-sized computer. In ACM SIGARCH Computer Architecture News(Vol. 35, No. 2, pp. 13-23). ACM. Ardagna, D., Panicucci, B., Trubian, M. and Zhang, L., 2012. Energy-aware autonomic resource allocation in multitier virtualized environments. Services Computing, IEEE Transactions on, 5(1), pp.2-19.
Forsman, M., Glad, A., Lundberg, L. and Ilie, D., 2015. Algorithms for automated live migration of virtual machines. Journal of Systems and Software, 101, pp.110-126.
Ghribi, C., Hadji, M. and Zeghlache, D., 2013, May. Energy efficient VM scheduling for cloud data centers: Exact allocation and migration algorithms. In Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on (pp. 671-678). IEEE.
Goudarzi, H., Ghasemazar, M. and Pedram, M., 2012, May. Sla-based optimization of power and migration cost in cloud computing. In Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on (pp. 172-179). IEEE.
Graubner, P., Schmidt, M. and Freisleben, B., 2013. Energy-efficient virtual machine consolidation. IT Professional, (2), pp.28-34.
Hu, L., Jin, H., Liao, X., Xiong, X. and Liu, H., 2008, September. Magnet: A novel scheduling policy for power reduction in cluster with virtual machines. In Cluster Computing, 2008 IEEE International Conference on (pp. 13-22). IEEE.
Kelton, W.D., Sadowski, R.P. and Sadowski, D.A., 2002. Simulation with ARENA. McGraw-Hill, Inc..
Kim, K.H., Beloglazov, A. and Buyya, R., 2009, November. Power-aware provisioning of cloud resources for real-time services. In Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science, pp. 1-6,. ACM.
Koomey, J.G., 2007. Estimating total power consumption by servers in the US and the world.
Lin, C.C., Liu, P. and Wu, J.J., 2011, July. Energy-aware virtual machine dynamic provision and scheduling for cloud computing. In Cloud Computing (CLOUD), 2011 IEEE International Conference on (pp. 736-737). IEEE.
Liu, Z., Wierman, A., Chen, Y., Razon, B. and Chen, N., 2013. Data center demand response: Avoiding the coincident peak via workload shifting and local generation. Performance Evaluation, 70(10), pp.770-791.
Malleswari, T.N., Vadivu, G. and Malathi, D., 2015. Live Virtual Machine Migration Techniques—A Technical Survey. In Intelligent Computing, Communication and Devices (pp. 303-319). Springer India.
Menascé, D.A., 2005, December. Virtualization: Concepts, applications, and performance modeling. In Int. CMG Conference (pp. 407-414).
Patterson, D.A., 2008. The data center is the computer. Communications of the ACM, 51(1), pp.105-105.
Sahu, Y., Pateriya, R.K. and Gupta, R.K., 2013, September. Cloud server optimization with load balancing and green computing techniques using dynamic compare and balance algorithm. In Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on (pp. 527-531). IEEE.
Singh, A., Korupolu, M. and Mohapatra, D., 2008, November. Server-storage virtualization: integration and load balancing in data centers. In Proceedings of the 2008 ACM/IEEE conference on Supercomputing (p. 53). IEEE Press.
Stanford PowerNet project, dataset available http://sing.stanford.edu/maria/powernet/ available online accessed on 10 October 2015.
Stoess, J., Lang, C. and Bellosa, F., 2007, June. Energy Management for Hypervisor-Based Virtual Machines. In USENIX annual technical conference(pp. 1-14).
Takeda, S. and Takemura, T., 2010. A rank-based vm consolidation method for power saving in datacenters. Information and Media Technologies, 5(3), pp.994-1002.
Wang, J., Huang, C., Liu, Q., He, K., Wang, J., Li, P. and Jia, X., 2014. An Optimization VM Deployment for Maximizing Energy Utility in Cloud Environment. In Algorithms and Architectures for Parallel Processing (pp. 400-414). Springer International Publishing.
Wang, X., Du, Z., Chen, Y. and Yang, M., 2015. A green-aware virtual machine migration strategy for sustainable datacenter powered by renewable energy. Simulation Modelling Practice and Theory, 58, pp.3-14
Wiedmann, T. and Minx, J., 2008. A definition of ‘carbon footprint’. Ecological economics research trends, 1, pp.1-11.
Wood, T., Shenoy, P., Venkataramani, A. and Yousif, M., 2009. Sandpiper: Black-box and gray-box resource management for virtual machines. Computer Networks, 53(17), pp.2923-2938.
Wood, T., Shenoy, P.J., Venkataramani, A. and Yousif, M.S., 2007, April. Black-box and Gray-box Strategies for Virtual Machine Migration. In NSDI(Vol. 7, pp. 17-17).
Ye, K., Huang, D., Jiang, X., Chen, H. and Wu, S., 2010, December. Virtual machine based energy-efficient data center architecture for cloud computing: a performance perspective. In Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing (pp. 171-178). IEEE Computer Society.
Zou, S., Wen, X., Chen, K., Huang, S., Chen, Y., Liu, Y., Xia, Y. and Hu, C., 2014. Virtualknotter: Online virtual machine shuffling for congestion resolving in virtualized datacenter. Computer networks, 67, pp.141-153.
