Enhancing Energy Efficiency in Cloud Data Centers: The Role of Virtualization in Sustainable Computing

  • Male Henry Kenneth ISBAT University
  • Kahangi Martin ISBAT University
Keywords: Energy Efficiency, Cloud Data Centers, Virtualization, Resource Utilization, Sustainable Computing
Share Article:

Abstract

The increasing demand for cloud computing services has led to the exponential growth of data centres, which consume substantial amounts of energy and contribute to environmental concerns. This project investigates the potential of virtualization technology to enhance the energy efficiency of cloud data centres. By abstracting physical resources and creating virtual machines, virtualization allows for the dynamic allocation and consolidation of workloads, leading to optimized resource utilization and reduced energy consumption. This research explores various virtualization techniques, their implementation in cloud environments, and their impact on energy efficiency. Through a series of simulations and real-world case studies, the project aims to quantify the energy savings achieved and identify best practices for deploying virtualization to create more sustainable cloud data centres. The findings suggest that adopting virtualization not only lowers operational costs but also aligns with global efforts to reduce carbon footprints, making it a crucial strategy for the future of cloud computing infrastructure

Downloads

Download data is not yet available.

References

Akhter, N., Othman, M., & Naha, R. K. (2018). Energy-aware virtual machine selection method for cloud data center resource allocation (arXiv:1812.08375). arXiv. https://doi.org/10.48550/arXiv.1812.08375

Ali, S. A., Affan, M., & Alam, M. (2019). A Study of Efficient Energy Management Techniques for Cloud Computing Environment. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 13– 18. https://doi.org/10.1109/CONFLUENCE.2019.8776977

Canosa-Reyes, R. M., Tchernykh, A., Cortés-Mendoza, J. M., Pulido-Gaytan, B., Rivera-Rodriguez, R., Lozano-Rizk, J. E., Concepción-Morales, E. R., Barrera, H. E. C., Barrios-Hernandez, C. J., Medrano-Jaimes, F., Avetisyan, A., Babenko, M., & Drozdov, A. Y. (2022). Dynamic performance–Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds. PLOS ONE, 17(1), e0261856. https://doi.org/10.1371/journal.pone.0261856

Foster, I., Freeman, T., Keahy, K., Scheftner, D., Sotomayer, B., & Zhang, X. (2006). Virtual Clusters for Grid Communities. Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID’06), 513–520. https://doi.org/10.1109/CCGRID.2006.108

Gandhi, A., Harchol-Balter, M., Raghunathan, R., & Kozuch, M. A. (2012). AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers. ACM Trans. Comput. Syst., 30(4), 14:1-14:26. https://doi.org/10.1145/2382553.2382556

Goyal, S., Bhushan, S., Kumar, Y., Rana, A. ul H. S., Bhutta, M. R., Ijaz, M. F., & Son, Y. (2021). An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm. Sensors, 21(5), Article 5. https://doi.org/10.3390/s21051583

Hijji, M., Ahmad, B., Alam, G., Alwakeel, A., Alwakeel, M., Abdulaziz Alharbi, L., Aljarf, A., & Khan, M. U. (2022). Cloud Servers: Resource Optimization Using Different Energy Saving Techniques. Sensors, 22(21), Article 21. https://doi.org/10.3390/s22218384

Kusic, D., Kephart, J. O., Hanson, J. E., Kandasamy, N., & Jiang, G. (2009). Power and performance management of virtualized computing environments via lookahead control. Cluster Computing, 12(1), 1–15. https://doi.org/10.1007/s10586-008-0070-y

Liaqat, M., Ninoriya, S., Shuja, J., Ahmad, R. W., & Gani, A. (2016). Virtual Machine Migration Enabled Cloud Resource Management: A Challenging Task (arXiv:1601.03854). arXiv. https://doi.org/10.48550/arXiv.1601.03854

Naji, H. R., & Esmaeili, R. (2023). Reducing energy consumption of cloud data centers using proper placement of virtual machines (arXiv:2311.17282). arXiv. https://doi.org/10.48550/arXiv.2311.17282

Nanduri, R., Kakadia, D., & Varma, V. (2014). Energy and SLA aware VM Scheduling (arXiv:1411.6114). arXiv. https://doi.org/10.48550/arXiv.1411.6114

Stana, M., Sepiol, B., Kozubski, R., & Leitner, M. (2016). Chemical ordering beyond the superstructure in long-range ordered systems. New Journal of Physics, 18(11), 113051. https://doi.org/10.1088/1367-2630/18/11/113051

Usman Sana, M., & Li, Z. (2021). Efficiency aware scheduling techniques in cloud computing: A descriptive literature review. PeerJ Computer Science, 7, e509. https://doi.org/10.7717/peerj-cs.509

Zhou, Q., Xu, M., Singh Gill, S., Gao, C., Tian, W., Xu, C., & Buyya, R. (2020). Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 489– 498. https://doi.org/10.1109/CCGrid49817.2020.00-44

Published
7 April, 2025
How to Cite
Kenneth, M., & Martin, K. (2025). Enhancing Energy Efficiency in Cloud Data Centers: The Role of Virtualization in Sustainable Computing. East African Journal of Information Technology, 8(1), 22-34. https://doi.org/10.37284/eajit.8.1.2836