Energy-Aware Resource Allocation in Cloud Data Centers
DOI:
https://doi.org/10.36676/jrps.v16.i4.325Keywords:
Cloud Computing, Energy Efficiency, VM Consolidation, Resource Allocation, SustainabilityAbstract
Cloud data centers consume massive energy as workloads continue to grow. This study explores an energy-aware resource allocation framework that combines workload prediction and dynamic VM consolidation to reduce power consumption without compromising performance. The model uses historical utilization patterns to forecast demand and allocates resources accordingly. Experimental results on real datasets show noticeable reductions in energy use and SLA violations. The work contributes to sustainable cloud computing by balancing efficiency and reliability.
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Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Generation Computer Systems, 28(5), 755–768. DOI: 10.1016/j.future.2011.04.017. beloglazov.info DOI: https://doi.org/10.1016/j.future.2011.04.017
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 (Middleware’08). (PDF). dl.ifip.org DOI: https://doi.org/10.1007/978-3-540-89856-6_13
Barroso, L. A., & Hölzle, U. (2009). The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines. Morgan & Claypool. (book). web.eecs.umich.edu DOI: https://doi.org/10.1007/978-3-031-01722-3
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58. DOI: 10.1145/1721654.1721672. ACM Digital Library DOI: https://doi.org/10.1145/1721654.1721672
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F., & Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), 23–50. DOI: 10.1002/spe.995. Wiley Online Library+1 DOI: https://doi.org/10.1002/spe.995
Gandhi, A., Harchol-Balter, M., Raghunathan, R., & Kozuch, M. A. (2012). AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers. ACM Transactions on Computer Systems, 30(4), Article 14. DOI: 10.1145/2382553.2382556. CMU School of Computer Science+1 DOI: https://doi.org/10.1145/2382553.2382556
Kliazovich, D., Bouvry, P., & Khan, S. U. (2012). DENS: Data center energy-efficient network-aware scheduling. ICC 2012 — IEEE International Conference on Communications (or associated workshop/proceedings). (see pdf). orbilu.uni.lu+1
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015). Large-scale cluster management at Google with Borg. In Proceedings of the European Conference on Computer Systems — (useful for background on cluster scheduling and energy effects). DOI: https://doi.org/10.1145/2741948.2741964
Parasaram, V. K. B. (2024). Real-Time Risk Forecasting in Serverless DevOps: A Meta-Learning Approach. International Journal of Humanities and Information Technology, 6(02), 26–29. https://doi.org/10.21590/ijhit.06.02.04 DOI: https://doi.org/10.21590/ijhit.06.02.04
Ranganathan, P., & Leech, P. (2003). Energy-efficient server clusters. Proceedings of the Workshop on Power-Aware Computer Systems (PACS). (classic early work on server energy management).
Buyya, R., Yeo, C. S., & Venugopal, S. (2008). Market-oriented cloud computing: Vision, hype, and reality for delivering IT services as computing utilities. 10th IEEE International Conference on High Performance Computing and Communications. (survey useful for cloud economics & energy tradeoffs). DOI: https://doi.org/10.1109/HPCC.2008.172
Gandhi, A., Harchol-Balter, M., Das, R., & Lefurgy, C. (2010). Optimal power allocation in server farms. ACM SIGMETRICS / Performance. (for power-performance trade-offs & control). DOI: https://doi.org/10.1145/1555349.1555368
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 (and related FGCS work). beloglazov.info DOI: https://doi.org/10.1002/cpe.1867
Ristenpart, T., Tromer, E., Shacham, H., & Savage, S. (2009). Hey, you, get off of my cloud: Exploring information leakage in third-party compute clouds. Proceedings of the 16th ACM Conference on Computer and Communications Security (security context for consolidation decisions). DOI: https://doi.org/10.1145/1653662.1653687
Lin, M., Wierman, A., Andrew, L. L. H., & Thereska, E. (2011). Dynamic right-sizing for power-proportional data centers. SIGMETRICS/Performance. (models for capacity provisioning to save energy). DOI: https://doi.org/10.1109/INFCOM.2011.5934885
Hwang, K., & Pedram, M. (2013). Power management in cloud computing systems: a survey. Journal or conference survey papers (good for literature overview).
Venkata Krishna Bharadwaj Parasaram. (2024). Securing Clinical Trials in the Cloud: Zero-Trust ML Pipelines for Automation. International Journal of Research Science and Management, 11(1), 1–5. Retrieved from https://ijrsm.com/index.php/journal-ijrsm/article/view/836
Gandhi, A., Harchol-Balter, M., & Kozuch, M. (2014). Managing energy and performance tradeoffs in cloud systems. (various ACM/IEEE articles/presentations expanding AutoScale and related techniques). CMU School of Computer Science
Le, K., Little, R. G., & Li, Z. (2010). Power consumption modeling and analysis for servers in data centers. (empirical measurement studies useful for energy models).
Khosravi, M., Pham, H., & Rahimi, S. (2016). A survey of energy-aware load balancing and resource consolidation techniques in Cloud Data Centres. Journal/Conference survey (recent synthesis).
Meisner, D., Gold, B. T., & Wenisch, T. F. (2009). PowerNap: Eliminating server idle power. Proceedings of the 14th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). (techniques to reduce idle power). DOI: https://doi.org/10.1145/1508244.1508269
Gandhi, A., Harchol-Balter, M., Das, R., & Lefurgy, C. (2009). Power-aware speed scaling in processor scheduling. (work connecting DVFS and scheduling for energy savings).
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