Enhancing Cloud Energy Efficiency through Predictive Machine Learning for Inter- and Intra-Data Center VM Consolidation

The rapid expansion of Cloud Computing and large-scale data centers has resulted in a substantial increase in energy consumption, primarily due to hardware operation and cooling requirements. This rise in power usage has significantly elevated operational costs, making energy efficiency a critical concern for data center management. Virtual Machine (VM) consolidation is a well-established strategy to address these challenges by reducing the number of active physical servers while ensuring compliance with Service Level Agreements (SLAs). However, the effectiveness of consolidation heavily depends on the accurate prediction of VM resource demands. This paper proposes a consolidation approach—encompassing both intra- and inter-data center strategies—for energy-aware VM allocation across physical servers. The system leverages predictive machine learning models to forecast future computational needs of individual VMs. By anticipating these demands, the framework dynamically allocates VMs across the servers of the considered data centers to optimize server utilization and minimize energy consumption, without compromising performance or SLA compliance. Preliminary experimental results demonstrate that the proposed approach significantly reduces overall power consumption, particularly when guided by machine learning-driven workload forecasting.