.
| ESP Journal of Engineering & Technology Advancements |
| © 2026 by ESP JETA |
| Volume 6 Issue 3 |
| Year of Publication : 2026 |
| Author : Ratan Raj Anandeshi |
|
Ratan Raj Anandeshi, 2026. Energy-Aware Bare-Metal Provisioning in Virtualized Data Centers, Volume 6 Issue 3: 25-34.
Energy-conscious bare-metal provisioning has become an important research question since virtualized data centers are now full of mixed workloads, whose performance, isolation, and energy usage vary radically across virtual machines, containers, and direct hardware allocation. Traditional consolidation-based resource management assumes that more virtualization and higher resource utilization will always improve efficiency; however, more recent work shows this does not hold when boot latency, migration overhead, thermal characteristics, I/O interference, and workload heterogeneity are factored in. This paper reviews the current state of research on this topic in energy-aware bare-metal provisioning in virtualized data-centers and has a special focus on provisioning delay, architecture choice, power-aware scheduling, placement optimization, thermal interrelations and hybrid orchestration between bare-metal and virtualized resources. Key topics include energy modeling, dynamic resource consolidation, VM placement, bare metal reservation, hybrid resource management, and the activation-energy trade-off. Reported research suggests that energy efficiency depends heavily on server utilization, provisioning granularity, interference sensitivity, data center wide orchestration policies, and the ability to reserve non-virtualized nodes for latency-sensitive workloads.
[1] Kaur, T., & Chana, I. (2015). Energy efficiency techniques in cloud computing: A survey and taxonomy. ACM Computing Surveys, 48(2), Article 22.
[2] Sharma, Y., Javadi, B., Si, W., & Sun, D. (2016). Reliability and energy efficiency in cloud computing systems: Survey and taxonomy. Journal of Network and Computer Applications, 74, 66–85.
[3] Silva Filho, M. C., Monteiro, C. C., Inácio, P. R. M., & Freire, M. M. (2018). Approaches for optimizing virtual machine placement and migration in cloud environments: A survey. Journal of Parallel and Distributed Computing, 111, 222–250.
[4] Castañé, G. G., Núñez, A., Llopis, P., & Carretero, J. (2013). E-mc2: A formal framework for energy modelling in cloud computing. Simulation Modelling Practice and Theory, 39, 56–75.
[5] de Assunção, M. D., & Lefèvre, L. (2018). Bare-metal reservation for cloud: An analysis of the trade-off between reactivity and energy efficiency. Cluster Computing, 21(2), 1289–1300.
[6] Sîrbu, A., Pop, C., Şerbănescu, C., & Pop, F. (2017). Predicting provisioning and booting times in a Metal-as-a-Service system. Future Generation Computer Systems, 72, 180–192.
[7] Yamato, Y. (2017). Performance-aware server architecture recommendation and automatic performance verification technology on IaaS cloud. Service Oriented Computing and Applications, 11(2), 121–135.
[8] Lee, Y. C., & Zomaya, A. Y. (2012). Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60(2), 268–280.
[9] 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.
[10] Zhao, H., Wang, J., Liu, F., Wang, Q., Zhang, W., & Zheng, Q. (2018). Power-aware and performance-guaranteed virtual machine placement in the cloud. IEEE Transactions on Parallel and Distributed Systems, 29(6), 1385–1400.
[11] Xiao, P., & Liu, D. (2013). An energy-efficient virtual machine scheduler with I/O collective mechanism in resource virtualisation environments. International Journal of Networking and Virtual Organisations, 13(4), 311–326.
[12] Feng, H., Deng, Y., & Li, J. (2021). A global-energy-aware virtual machine placement strategy for cloud data centers. Journal of Systems Architecture, 116, Article 102048.
[13] Qiu, Y., Jiang, C., Wang, Y., Ou, D., Li, Y., & Wan, J. (2019). Energy aware virtual machine scheduling in data centers. Energies, 12(4), Article 646.
[14] Shaw, R., Howley, E., & Barrett, E. (2019). An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions. Simulation Modelling Practice and Theory, 93, 322–342.
[15] Shaw, R., Howley, E., & Barrett, E. (2022). Applying reinforcement learning towards automating energy efficient virtual machine consolidation in cloud data centers. Information Systems, 107, Article 101722.
[16] Chen, R., Liu, B., Lin, W., Lin, J., Cheng, H., & Li, K. (2023). Power and thermal-aware virtual machine scheduling optimization in cloud data center. Future Generation Computer Systems, 145, 578–589.
[17] Wang, B., Liu, F., Lin, W., Ma, Z., & Xu, D. (2021). Energy-efficient collaborative optimization for VM scheduling in cloud computing. Computer Networks, 201, Article 108565.
[18] Khan, A. A., Zakarya, M., Buyya, R., Khan, R., Khan, M., & Rana, O. F. (2021). An energy and performance aware consolidation technique for containerized datacenters. IEEE Transactions on Cloud Computing, 9(4), 1305–1322.
[19] Khan, A. A., Zakarya, M., Rahman, I. U., Khan, R., & Buyya, R. (2021). HeporCloud: An energy and performance efficient resource orchestrator for hybrid heterogeneous cloud computing environments. Journal of Network and Computer Applications, 173, Article 102869.
[20] Katal, A., Dahiya, S., & Choudhury, T. (2023). Energy efficiency in cloud computing data centers: A survey on software technologies. Cluster Computing, 26(3), 1845–1875.
Bare-Metal Provisioning, Cloud Data Centers, Energy Efficiency, Resource Orchestration, Virtualized Infrastructure