Energy Efficient VM Migration Techniques in Cloud Computing Using Adaptive Threshold Algorithms
Keywords:
Cloud Computing, Energy Efficiency, Virtual Machine Migration, Resource Scheduling, CloudSim, Adaptive Threshold, IQR, Local RegressionAbstract
Cloud computing has revolutionized IT infrastructure by offering scalable and on-demand computing resources. However, the massive growth of cloud data centers has also led to huge power consumption, idle server inefficiencies and increased carbon footprint. Research indicates that an idle server can consume nearly 70% of its peak power, emphasizing the urgent need for energy-efficient resource allocation strategies. This paper proposes and evaluates VM (Virtual Machine) migration techniques using adaptive overload detection algorithms—Interquartile Range (IQR) and Local Regression (LR)—along with VM selection policies such as Minimum Migration Time (MMT), Maximum Correlation (MC) and Random Choice (RC). Using the CloudSim simulator and real workload traces from PlanetLab, experiments demonstrate that adaptive VM migration significantly reduces power consumption while minimizing service-level agreement (SLA) violations. The study concludes that dynamic resource scheduling through efficient VM placement and migration is essential for sustainable and cost-effective cloud operations.
References
• Armbrust, M., et al. (2009). Above the Clouds: A Berkeley View of Cloud Computing. University of California.
• 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.
• Buyya, R., Yeo, C.S., & Venugopal, S. (2008). Market-Oriented Cloud Computing: Vision, Hype and Reality. Proceedings of HPCC.
• Calheiros, R.N., et al. (2011). CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. Software: Practice and Experience.
• Kliazovich, D., Bouvry, P., & Khan, S.U. (2013). GreenCloud: A Packet-level Simulator of Energy-aware Cloud Computing Data Centers. Journal of Supercomputing.
• Liu, L. (2018). Energy-Aware Scheduling for Cloud Computing Systems. Future Generation Computer Systems.
• Singh, S., Chana, I. (2015). QoS-aware Resource Provisioning in Cloud Computing: Taxonomy and Classification. Journal of Network and Computer Applications.
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