Research on Energy-aware Resource Scaling in Cloud Computing focuses on developing strategies to dynamically adjust cloud resources while minimizing energy consumption and operational costs, without compromising performance or Quality of Service (QoS). This area addresses the challenges of fluctuating workloads, heterogeneous resources, and large-scale cloud infrastructures. Key research directions include designing energy-efficient auto-scaling algorithms, predictive resource provisioning using machine learning, and SLA-aware scaling strategies that balance energy use with application performance. Other emerging topics involve green cloud computing approaches, dynamic consolidation of virtual machines and containers, energy-aware load balancing, and integration of edge and cloud resources for optimized energy consumption. Additionally, research on multi-objective optimization considering energy, cost, and performance trade-offs, fault-tolerant energy-aware scaling, and reinforcement learning-based adaptive scaling frameworks represents promising avenues for sustainable and efficient cloud resource management.