Research on DVFS-aware Server Consolidation in Cloud Computing focuses on optimizing the placement and operation of virtual machines (VMs) across physical servers while leveraging Dynamic Voltage and Frequency Scaling (DVFS) to reduce energy consumption and operational costs. This area addresses the challenges of balancing energy efficiency with performance, QoS, and SLA compliance in dynamic and heterogeneous cloud environments. Key research directions include designing DVFS-aware consolidation algorithms that adaptively adjust CPU frequencies based on workload characteristics, heuristic and metaheuristic approaches for energy-efficient VM placement, and predictive workload modeling for proactive server consolidation. Other emerging topics involve multi-objective optimization balancing energy, performance, and thermal management, integrating DVFS-aware consolidation with cloud–edge infrastructures, and combining DVFS with live VM migration for dynamic energy savings. Additionally, research on fault-tolerant, SLA-compliant, and machine learning-enhanced DVFS strategies represents promising avenues for advancing sustainable and efficient cloud data center management.