Integrated Green Cloud Framework using Virtualization, Carbon-Aware Workload Migration, and Simulation for Sustainable Data Centers.
DOI:
https://doi.org/10.70454/JRIST.2025.10205Keywords:
Green Cloud Computing, Virtualization; Carbon-Aware Scheduling, Sustainable Data Centers, Workload Migration, CloudSim Simulation, Energy Efficiency, Carbon Emission ReductionsAbstract
The unforeseen growth of digital services has escalated energy requirements in the world data centers, and an environmental sustainability issue is a hot topic in cloud computing. Conventional parallelism to workload scheduling fails to consider real time carbon intensity and regional grid efficiency thus resulting in high carbon emission even in optimally performed computational work. The proposed work suggests a green cloud synergetic framework, which integrates the virtualization, the carbon-aware workload migration, and the simulation-based decision model to plain the challenge of energy inefficiency and the environmental impact associated with the data center operations. This framework exploits a dynamic virtualization layer to achieve VM consolidation, as well as a carbon-conscious migrator that on real-time emissions data migrates workloads to utilise cleaner regional grids. CloudSim Plus-supported simulation environment was created to test the model in terms of its operation over the 24-hour period, with the testing against baseline and partial setup. The findings show the decreased energy usage of 36.6 percent and a decreased carbon emission of 52 percent in comparison with the conventional static allocation plan. The added migration events do not affect significantly the model as it continues to sustain high VM utilization (72.3%) and low SLA violations (8.1%). The effectiveness of the proposed framework compared to the available scheduling models is proven by the matter of fact that the former performs better in terms of various tracks of sustainability according to benchmarking. The paper presents a flexible and elastic approach to a sustainable cloud infrastructure and forms the basis of future-generation carbon-minded orchestration in the cloud.
References
[1] Buyya, R., Beloglazov, A., & 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.
[2] Jin, X., Zhang, F., Vasilakos, A. V., & Liu, Z. (2016). Green data centers: A survey, perspectives, and future directions. arXiv preprint arXiv:1608.00687.
[3] Hanafy, W. A., Liang, Q., Bashir, N., Irwin, D., & Shenoy, P. (2023). CarbonScaler: Leveraging cloud workload elasticity for optimizing carbon-efficiency. arXiv preprint arXiv:2302.08681.
[4] Uddin, M., & Rahman, A. A. (2012). Virtualization implementation model for cost-effective & efficient data centers. arXiv preprint arXiv:1206.0988.
[5] Shuja, J., Gani, A., Shamshirband, S., Ahmad, R. W., & Bilal, K. (2016). Sustainable cloud data centers: A survey of enabling techniques and technologies. Renewable and Sustainable Energy Reviews, 62, 195–214.
[6] 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.
[7] Li, B., Li, J., Hu, Y. C., & Liu, J. (2012). Enabling efficient and scalable hybrid cloud CDN. IEEE Transactions on Parallel and Distributed Systems, 23(7), 1297–1305.
[8] Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.
[9] Xu, J., & Fortes, J. A. (2010). Multi-objective virtual machine placement in virtualized data center environments. 2010 IEEE/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing, 179–188.
[10] Gandhi, A., Harchol-Balter, M., Das, R., & Lefurgy, C. (2010). Optimal power allocation in server farms. Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 157–168.
[11] Verma, A., Ahuja, P., & Neogi, A. (2008). pMapper: Power and migration cost aware application placement in virtualized systems. Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, 243 264.
[12] Chase, J. S., Anderson, D. C., Thakar, P. N., Vahdat, A. M., & Doyle, R. P. (2001). Managing energy and server resources in hosting centers. Proceedings of the 18th ACM Symposium on Operating Systems Principles, 103 116.
[13] Barroso, L. A., & Hölzle, U. (2007). The case for energy-proportional computing. IEEE Computer, 40(12), 33 37.
[14] Meisner, D., Gold, B. T., & Wenisch, T. F. (2009). PowerNap: Eliminating server idle power. ACM SIGPLAN Notices, 44(3), 205–216.
[15] Ranganathan, P., Leech, P., Irwin, D., & Chase, J. S. (2006). Ensemble-level power management for dense blade servers. ACM SIGARCH Computer Architecture News, 34(2), 66–77.
[16] Chidolue, O., Ohenhen, P. E., Umoh, A. A., Ngozichukwu, B., Fafure, A. V., & Ibekwe, K. I. (2024). Green data centers: Sustainable practices for energy-efficient IT infrastructure. Engineering Science & Technology Journal, 5(1), 99–114.
[17] Chowdhury, M. R., & Rahman, M. M. (2022). A comprehensive review of green computing: Past, present, and future research. IEEE Access, 11, 87445–87466.
[18] Chowdhury, M. R., & Rahman, M. M. (2022). A systematic survey on energy-efficien techniques in sustainable cloud computing. Sustainability, 14(10), 6256.
[19] Chowdhury, M. R., & Rahman, M. M. (2022). An energy and carbon-aware algorithm for renewable energy usage in cloud data centers. Journal of Systems Architecture, 127, 102456.
[20] Chowdhury, M. R., & Rahman, M. M. (2022). Carbon-aware spatio-temporal workload shifting in edge computing. Sustainability, 14(14), 6433.
[21] Chowdhury, M. R., & Rahman, M. M. (2022). CASPER: Carbon-aware scheduling and provisioning for distributed web services. arXiv preprint arXiv:2403.14792.
[22] Chowdhury, M. R., & Rahman, M. M. (2022). Energy efficiency in cloud computing data centers: A survey on hardware technologies. Cluster Computing, 25(1), 1–20.
[23] Chowdhury, M. R., & Rahman, M. M. (2022). Energy-efficient algorithms based on VM consolidation for cloud computing: Comparisons and evaluations. arXiv preprint arXiv:2002.04860.
[24] Chowdhury, M. R., & Rahman, M. M. (2022). Green cloud computing: A literature survey. Symmetry, 9(12), 295.
[25] Chowdhury, M. R., & Rahman, M. M. (2022). Modeling the green cloud continuum: Integrating energy efficiency and sustainability in cloud-edge systems. Cluster Computing, 25(2), 1–15.
[26] Chowdhury, M. R., & Rahman, M. M. (2022). Recent advances in energy-efficient resource management techniques in cloud computing environments. arXiv preprint arXiv:2107.06005.
[27] Gholipour, N., Arianyan, E., & Buyya, R. (2021). Recent advances in energy-efficient resource management techniques in cloud computing environments. arXiv preprint arXiv:2107.06005.
[28] Hossain, M. S., & Rahman, M. A. (2022). Modeling the green cloud continuum: Integrating energy efficiency and sustainability in cloud-edge systems. Cluster Computing, 25(2), 1–15.
[29] Mishra, S., & Sahoo, B. (2022). Energy efficiency in cloud computing data centers: A survey on hardware technologies. Cluster Computing, 25(1), 1–20.
[30] Paul, S. G., Reza, A. W., & Islam, M. S. (2023). A comprehensive review of green computing: Past, present, and future research. IEEE Access, 11, 87445–87466.
[31] Radu, L. D., & Radu, M. (2017). Green cloud computing: A literature survey. Symmetry, 9(12), 295.
[32] Souza, A., Jasoria, S., Chakrabarty, B., Bridgwater, A., Lundberg, A., Skogh, F., Ali-Eldin, A., Irwin, D., & Shenoy, P. (2024). CASPER: Carbon-aware scheduling and provisioning for distributed web services. arXiv preprint arXiv:2403.14792.
[33] Zhou, Q., Xu, M., Gill, S. S., Gao, C., Tian, W., Xu, C., & Buyya, R. (2020). Energy-efficient algorithms based on VM consolidation for cloud computing: Comparisons and evaluations. arXiv preprint arXiv:2002.04860.
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