Introducing a Markov Chain-Based Energy Awareness Approach for Cloud Data Center Virtual Machine Dynamic Management

Dynamic management of virtual machines (VMs) in cloud data centers is essential for achieving flexibility, efficiency, and high productivity in cloud systems. With the rapid growth of cloud technologies and increasing demand for cloud-based services, effective resource management has become a critical factor for ensuring quality service delivery while minimizing operational costs. This is particularly important for organizations seeking to optimize their resource utilization and adapt dynamically to fluctuating workloads. This paper introduces a novel model designed to address these challenges by enhancing existing algorithms and employing advanced techniques. The approach integrates genetic algorithms and refrigeration simulation into the migration replacement process, leveraging absorbing Markov chains for predictive analysis. By continuously monitoring resource status, analyzing incoming data, and forecasting critical server conditions, the model effectively reduces unnecessary VM migrations. Simulations conducted using the Clodsim environment demonstrate the model’s efficiency in reducing energy consumption across low, medium, and high-load scenarios. The proposed method achieves an average reduction in energy consumption of 17% compared to state-of-the-art methods, while also minimizing violations of service-level agreements (SLA). This research highlights the importance of combining predictive analytics with robust optimization techniques to improve cloud resource management. By achieving significant energy savings and enhancing system reliability, the proposed model offers a practical, sustainable framework for dynamic VM management, addressing the challenges posed by growing user demands and resource constraints in modern cloud data centers.