An Overview of the State-of-the-art Virtual Machine Placement Algorithms for Green Cloud Data Centres
DOI:
https://doi.org/10.46977/apjmt.2022v03i01.001%20Keywords:
Cloud Data Centres, Virtual Machine, Physical Machine, Server ConsolidationAbstract
Increased energy consumption in Cloud Data Centres (CDCs) increases the carbon footprint. Efficiency of the data centres thus needs to be improved through server consolidation using effective virtual machine (VM) placement and migration techniques and minimizing the number of active physical machines (PMs). One of the problems is how to operationally allocate the VMs to PMs. These allocations have both operational costs and energy consumption issues. To achieve the aim of ‘Green Computing’ a number of state-of-the-art machine learning algorithms have been proposed for the VM placement. The authors of this paper have provided a detailed discussion and comparison of some of the current research works on energy efficiency. and cons of each of these techniques have been discussed. Some future research prospects in this field have also been mentioned at the end.
Downloads
References
Beloglazov, A., & Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud Data Centres. Concurrency and Computation: Practice and Experience, 24(13), 1397-1420.
Bichler, M., Setzer, T., & Speitkamp, B. (2006). Capacity planning for virtualized servers. In Workshop on Information Technologies and Systems (WITS), Milwaukee, Wisconsin, USA.
Bobroff, N., Kochut, A., & Beaty, K. (2007, May). Dynamic placement of virtual machines for managing sla violations. In 2007 10th IFIP/IEEE International Symposium on Integrated Network Management (pp. 119-128). IEEE.
Buyya, R., Beloglazov, A., & Abawajy, J. (2010). Energy-efficient management of data centre resources for cloud computing: A vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308.
Chowdhury, M. R., Mahmud, M. R., & Rahman, R. M. (2015, June). Study and performance analysis of various VM placement strategies. In 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 1-6). IEEE.
Dong, J., Wang, H., & Cheng, S. (2015). Energy-performance tradeoffs in IaaS cloud with virtual machine scheduling. China communications, 12(2), 155-166.
Dupont, C., Hermenier, F., Schulze, T., Basmadjian, R., Somov, A., & Giuliani, G. (2015). Plug4green: A flexible energy-aware vm manager to fit data centre particularities. Ad Hoc Networks, 25, 505-519.
Dupont, C., Schulze, T., Giuliani, G., Somov, A., & Hermenier, F. (2012, May). An energy aware framework for virtual machine placement in cloud federated data centres. In 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy) (pp. 1-10). IEEE.
Fan, X., Weber, W. D., & Barroso, L. A. (2007). Power provisioning for a warehouse-sized computer. ACM SIGARCH Computer Architecture News, 35(2), 13-23.
Fang, W., Liang, X., Li, S., Chiaraviglio, L., & Xiong, N. (2013). VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in Cloud Data Centres . Computer Networks, 57(1), 179-196.
Feller, E., Rilling, L., & Morin, C. (2011, September). Energy-aware ant colony based workload placement in clouds. In 2011 IEEE/ACM 12th International Conference on Grid Computing (pp. 26-33). IEEE.
Ferdaus, M. H., Murshed, M., Calheiros, R. N., & Buyya, R. (2014, August). Virtual machine consolidation in Cloud Data Centres using ACO metaheuristic. In European conference on parallel processing (pp. 306-317). Springer, Cham.
Jing, S. Y., Ali, S., & She, K. (2013). Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Centre. In Applied Mechanics and Materials (Vol. 325, pp. 1730-1733). Trans Tech Publications Ltd.
Kansal, N. J., & Chana, I. (2016). Energy-aware virtual machine migration for cloud computing-a firefly optimization approach. Journal of Grid Computing, 14(2), 327-345.
Khosravi, A., Garg, S. K., & Buyya, R. (2013, August). Energy and carbon-efficient placement of virtual machines in distributed Cloud Data Centres . In European Conference on Parallel Processing (pp. 317-328). Springer, Berlin, Heidelberg.
Li, K., Zheng, H., & Wu, J. (2013, November). Migration-based virtual machine placement in cloud systems. In 2013 IEEE 2nd International Conference on Cloud Networking (CloudNet) (pp. 83-90). IEEE.
Liu, X. F., Zhan, Z. H., Du, K. J., & Chen, W. N. (2014, July). Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (pp. 41-48).
Liu, X., Gu, H., Zhang, H., Liu, F., Chen, Y., & Yu, X. (2017). Energy-Aware on-chip virtual machine placement for cloud-supported cyber-physical systems. Microprocessors and Microsystems, 52, 427-437.
Mi, H., Wang, H., Yin, G., Zhou, Y., Shi, D., & Yuan, L. (2010, July). Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centres . In 2010 IEEE International Conference on Services Computing (pp. 514-521). IEEE.
Mills, K., Filliben, J., & Dabrowski, C. (2011, November). Comparing vm-placement algorithms for on-demand clouds. In 2011 IEEE Third International Conference on Cloud Computing Technology and Science (pp. 91-98). IEEE.
Mishra, M., & Sahoo, A. (2011, July). On theory of vm placement: Anomalies in existing methodologies and their mitigation using a novel vector based approach. In 2011 IEEE 4th International Conference on Cloud Computing (pp. 275-282). IEEE.
Moges, F. F., & Abebe, S. L. (2019). Energy-aware VM placement algorithms for the OpenStack Neat consolidation framework. Journal of Cloud Computing, 8(1), 1-14.
Mosa, A., & Paton, N. W. (2016). Optimizing virtual machine placement for energy and SLA in clouds using utility functions. Journal of Cloud Computing, 5(1), 1-17.
Patel, N., & Patel, H. (2020). Energy efficient strategy for placement of virtual machines selected from underloaded servers in compute Cloud. Journal of King Saud University-Computer and Information Sciences, 32(6), 700-708.
Speitkamp, B., & Bichler, M. (2010). A mathematical programming approach for server consolidation problems in virtualized data centres . IEEE Transactions on Services Computing, 3(4), 266-278.
Tang, C., Steinder, M., Spreitzer, M., & Pacifici, G. (2007, May). A scalable application placement controller for enterprise data centres . In Proceedings of the 16th international conference on World Wide Web (pp. 331-340).
Usmani, Z., & Singh, S. (2016). A survey of virtual machine placement techniques in a cloud data centre. Procedia Computer Science, 78, 491-498.
Wang, S., Liu, Z., Zheng, Z., Sun, Q., & Yang, F. (2013, December). Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centres . In 2013 International Conference on Parallel and Distributed Systems (pp. 102-109). IEEE.
Wang, X., Chen, X., Yuen, C., Wu, W., & Wang, W. (2014, December). To migrate or to wait: Delay-cost tradeoff for Cloud Data Centres . In 2014 IEEE Global Communications Conference (pp. 2314-2319). IEEE.
Zhang, L., Zhuang, Y., & Zhu, W. (2013). Constraint programming based virtual cloud resources allocation model. International Journal of Hybrid Information Technology, 6(6), 333-344.
Zhao, J., Hu, L., Ding, Y., Xu, G., & Hu, M. (2014). A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment. PloS one, 9(9), e108275
Published
How to Cite
Issue
Section
Copyright (c) 2022 Asia-Pacific Journal of Management and Technology (AJMT)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.