Research on Workload-aware Energy Management in Cloud Computing focuses on designing strategies to optimize energy consumption in cloud data centers by dynamically adapting resource allocation and operational policies based on workload characteristics. This area addresses the challenges of fluctuating workloads, heterogeneous resources, and the need to balance energy efficiency with performance and Quality of Service (QoS) requirements. Key research directions include predictive workload modeling for proactive energy management, dynamic voltage and frequency scaling (DVFS) techniques, energy-aware task scheduling, and server consolidation strategies. Other emerging topics involve multi-objective optimization for balancing energy, cost, and performance, cloud–edge integrated energy management for latency-sensitive applications, and machine learning-driven adaptive frameworks for real-time energy optimization. Additionally, research on fault-tolerant, SLA-compliant, and green cloud computing approaches represents significant avenues for advancing sustainable and efficient cloud operations.