Abstract
Cloud computing is the on-demand availability of computing resources, especially storage and computing power. The Cloud Data Center (CDC) consumes a huge amount of electrical energy due to inefficient resource utilization and scheduling algorithms. This leads to issues like high energy consumption along with high maintenance costs and carbon emissions. Virtual machine consolidation (VMC) is one of the energy-saving approaches used in data centers. The VMC problem in CDCs with energy constraints is a strict NP-hard problem. In this paper, a dynamic VMC algorithm is proposed to address the issues discussed above. The proposed algorithm categorizes the servers into three categories such as underloaded, overloaded, and normally loaded based on the CPU load. It migrates a few virtual machines (VMs) from overloaded machines to the normally loaded machine(s) for load balancing and migrates all the VMs of underloaded machines to normally loaded servers to switch-off idle servers. Further, modified water wave optimization (MWWO) approach is used to find a suitable migration plan which will reduce the load on overloaded servers and increase the overall resource utilization. It has been known and proven that an overloaded host consumes more energy over some time than the normally utilized host. The experimental evaluation is done on CloudSim Plus which is a cloud simulation tool. We have used two real-time workloads for experiments to reveal the efficiency of the proposed MWWO-VMC algorithm. The simulation experimental results are compared with various heuristic and metaheuristic dynamic VM consolidation algorithms. In comparison to previous similar works, the MWWO-VMC algorithm is successful in reducing energy consumption and VM migrations, increasing resource utilization and maximizing the dormant servers.
Similar content being viewed by others
Availability of data and material
The data that support the findings of this study are openly available.
Code Availability
Custom code.
Data Availability
Enquiries about data availability should be directed to the authors.
References
Medara, R., & Singh, R. S. (2022). A review on energy-aware scheduling techniques for workflows in IaaS clouds. Wireless Personal Communications, 1–40.
Farahnakian, F., Ashraf, A., Pahikkala, T., Liljeberg, P., Plosila, J., Porres, I., & Tenhunen, H. (2015). Using ant colony system to consolidate VMs for green cloud computing. IEEE Transactions on Services Computing, 8(2), 187–198. https://doi.org/10.1109/TSC.2014.2382555
Gartner. (2021). Gartner forecasts worldwide public cloud end-user spending to grow 23% in 2021. https://www.gartner.com/en/newsroom/press-releases/2021-04-21-gartner-forecasts-worldwide-public-cloud-end-user-spendi-ng-to- grow-23-percent-in-2021.
Liu, Y., Wei, X., Xiao, J., Liu, Z., Yang, X., & Tian, Y. (2020). Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data centers. Global Energy Interconnection, 3(3), 272–282.
Lavi, H. (2022). Measuring greenhouse gas emissions in data centres: The environmental impact of cloud computing. https://www.climatiq.io/blog/measure-greenhouse-gas-emissionscarbon-data-centres-cloud-computing, Accessed 30 Dec 2022.
Liu, X.-F., Zhan, Z.-H., Deng, J. D., Li, Y., Tianlong, G., & Zhang, J. (2018). An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Transactions on Evolutionary Computation, 22(1), 113–128. https://doi.org/10.1109/TEVC.2016.2623803
Medara, R., Singh, R. S., & Sompalli, M. (2022). Energy and cost aware workflow scheduling in clouds with deadline constraint. Concurrency and Computation: Practice and Experience, e6922.
Choi, H., Lim, J., Yu, H., & Lee, E. (2016). Task classification based energy-aware consolidation in clouds. Scientific Programming.
Khan, M. A. (2021). An efficient energy-aware approach for dynamic VM consolidation on cloud platforms. Cluster Computing, 1–18.
Medara, R., Singh, R. S., Kumar, U. S., & Barfa, S. (2020). Energy efficient virtual machine consolidation using water wave optimization. In 2020 IEEE congress on evolutionary computation (CEC) (pp. 1–7). https://doi.org/10.1109/CEC48606.2020.9185865.
Zheng, Y.-J. (2015). Water wave optimization: A new nature-inspired metaheuristic. Computers & Operations Research, 55, 1–11.
Zhao, F., Zhang, L., Zhang, Y., Ma, W., Zhang, C., & Song, H. (2020). A hybrid discrete water wave optimization algorithm for the no-idle flowshop scheduling problem with total tardiness criterion. Expert Systems with Applications, 146, 113166.
Zhao, F., Zhang, L., Cao, J., & Tang, J. (2021). A cooperative water wave optimization algorithm with reinforcement learning for the distributed assembly no-idle flowshop scheduling problem. Computers & Industrial Engineering, 153, 107082.
Shao, Z., Pi, D., & Shao, W. (2019). A novel multi-objective discrete water wave optimization for solving multi-objective blocking flow-shop scheduling problem. Knowledge-Based Systems, 165, 110–131.
Zhou, X.-H., Zhang, M.-X., Zhi-Ge, X., Cai, C.-Y., Huang, Y.-J., & Zheng, Y.-J. (2019). Shallow and deep neural network training by water wave optimization. Swarm and Evolutionary Computation, 50, 100561.
Jin, Y., Li, S., & Ren, L. (2020). A new water wave optimization algorithm for satellite stability. Chaos, Solitons & Fractals, 138, 109793.
Soltanian, A., Derakhshan, F., & Soleimanpour-Moghadam, M. (2018). MWWO: Modified water wave optimization. In 2018 3rd Conference on swarm intelligence and evolutionary computation (CSIEC) (pp. 1–5). IEEE.
Silva Filho, M. C., Oliveira, R. L., Monteiro, C. C., Inácio, P. R. M., & Freire, M. M. (2017). Cloudsim plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness. In 2017 IFIP/IEEE symposium on integrated network and service management (IM) (pp. 400–406). https://doi.org/10.23919/INM.2017.7987304
Zahedi Fard, S. Y., Ahmadi, M. R., & Adabi, S. (2017). A dynamic VM consolidation technique for QoS and energy consumption in cloud environment. The Journal of Supercomputing, 73(10), 4347–4368.
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 centers. Concurrency and Computation: Practice and Experience, 24(13), 1397–1420.
Xiao, X., Zheng, W., Xia, Y., Sun, X., Peng, Q., & Guo, Yu. (2019). A workload-aware VM consolidation method based on coalitional game for energy-saving in cloud. IEEE Access, 7, 80421–80430.
Ding, W., Luo, F., Han, L., Chunhua, G., Haifeng, L., & Fuentes, J. (2020). Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers. Future Generation Computer Systems, 111, 254–270.
Azizi, S., Zandsalimi, M., & Li, D. (2020). An energy-efficient algorithm for virtual machine placement optimization in cloud data centers. Cluster Computing, 23(4), 3421–3434.
Arshad, U., Aleem, M., Srivastava, G., & Lin, J. C. W. (2022). Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers. Renewable and Sustainable Energy Reviews, 167, 112782.
Singh, S., & Kumar, R. (2022). Energy efficient optimization with threshold based workflow scheduling and virtual machine consolidation in cloud environment. Wireless Personal Communications, 1–22.
Mahdhi, T., & Mezni, H. (2018). A prediction-based VM consolidation approach in IaaS cloud data centers. Journal of Systems and Software, 146, 263–285.
Zhang, X., Tingming, W., Chen, M., Wei, T., Zhou, J., Shiyan, H., & Buyya, R. (2019). Energy-aware virtual machine allocation for cloud with resource reservation. Journal of Systems and Software, 147, 147–161.
Feng, H., Deng, Y., & Li, J. (2021). A global-energy-aware virtual machine placement strategy for cloud data centers. Journal of Systems Architecture, 116, 102048.
Shaw, R., Howley, E., & Barrett, E. (2022). Applying reinforcement learning towards automating energy efficient virtual machine consolidation in cloud data centers. Information Systems, 107, 101722.
Zeng, J., Ding, D., Kang, K., Xie, H., & Yin, Q. (2022). Adaptive DRL-based virtual machine consolidation in energy-efficient cloud data center. IEEE Transactions on Parallel and Distributed Systems, 33(11), 2991–3002.
Khemili, W., Hajlaoui, J. E., & Omri, M. N. (2022). Energy aware fuzzy approach for placement and consolidation in cloud data centers. Journal of Parallel and Distributed Computing, 161, 130–142.
Rawas, S. (2021). Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers. Multimedia Tools and Applications, 80(10), 15541–15562.
Torre, E., Durillo, J. J., Maio, V. D., Agrawal, P., Benedict, S., Saurabh, N., & Prodan, R. (2020). A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers. Information and Software Technology, 128, 106390.
Medara, R., & Shankar Singh, R. (2021). Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization. Simulation Modelling Practice and Theory, 110, 102323.
Singh, A. K., Swain, S. R. & Lee, C. N. (2022). A metaheuristic virtual machine placement framework toward power efficiency of sustainable cloud environment. Soft Computing, 1–12.
Ghetas, M. (2021). A multi-objective monarch butterfly algorithm for virtual machine placement in cloud computing. Neural Computing and Applications, 33(17), 11011–11025.
Sayadnavard, M. H., Haghighat, A. T., & Rahmani, A. M. (2022). A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers. Engineering Science and Technology, an International Journal, 26, 100995.
Li, Z., Yu, X., Lei, Y., Guo, S., & Chang, V. (2020). Energy-efficient and quality-aware VM consolidation method. Future Generation Computer Systems, 102, 789–809.
Haghighi, M. A., Maeen, M., & Haghparast, M. (2019). An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms. Wireless Personal Communications, 104(4), 1367–1391.
Balaras, C. A., Lelekis, J., Dascalaki, E. G., & Atsidaftis, D. (2017). High performance data centers and energy efficiency potential in Greece. Procedia Environmental Sciences, 38, 107–114.
Guérout, T., Monteil, T., Costa, G. D., Calheiros, R. N., Buyya, R., & Alexandru, M. (2013). Energy-aware simulation with DVFS. Simulation Modelling Practice and Theory, 39, 76–91.
Feitelson, D. G. & Nitzberg, B. (1995). Job characteristics of a production parallel scientific workload on the NASA Ames iPSC/860. In Workshop on job scheduling strategies for parallel processing (pp. 337–360). Springer.
LANL. (2021). The los alamos national lab (lanl) cm-5 log. https://www.lanl.gov/. Accessed 20 Oct 2021.
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We have no conflicts of interest associated with this publication.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Medara, R., Singh, R.S. Dynamic Virtual Machine Consolidation in a Cloud Data Center Using Modified Water Wave Optimization. Wireless Pers Commun 130, 1005–1023 (2023). https://doi.org/10.1007/s11277-023-10317-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-023-10317-3