An EID‐Infused and Energy‐Convergent VM Migration Strategy for Efficient DVMC in Virtualized Cloud Infrastructures

Modern cloud infrastructures must deliver energy‐efficient, high‐performance services while meeting Quality of Service (QoS) guarantees. However, dynamic workloads and heterogeneous hardware often create resource imbalances across hosts, triggering unnecessary virtual machine (VM) migrations, wasting energy, and increasing Service Level Agreement (SLA) violations. To address these challenges, this paper proposes an Energy‐Convergent migration framework for Dynamic VM Consolidation (DVMC) that integrates the Estimated Imbalance Degree (EID) with a probabilistic SLA Violation Detection Scheme (SDS). Our approach prioritizes migration using VM categorization with weight‐based criticality assessment. It employs mathematical models for demand estimation, Markov‐based load redistribution, bottleneck detection, forecasting, and energy‐aware migration planning. Formal theoretical analysis establishes the correctness of the framework, while efficient algorithms enable practical deployment under diverse cloud workloads. The proposed method accurately identifies host‐level imbalances and suppresses transient migrations through a unified threshold‐driven strategy. This approach enhances system stability and resource utilization. Experiments on OpenStack and CloudSim demonstrate average reductions of up to in energy consumption, in SLA violations, and in migrations compared with state‐of‐the‐art baselines, with the differences confirmed statistically at . Statistical and sensitivity analyses further confirm robustness and practical applicability.