Recent research in Energy-aware Resource Allocation in Cloud Computing focuses on optimizing the distribution of computational resources to minimize energy consumption while maintaining system performance and service-level agreements. These studies introduce intelligent algorithms that dynamically allocate resources based on workload intensity, VM utilization, and power efficiency. Techniques such as reinforcement learning, imitation-based optimization, and evolutionary algorithms are increasingly applied to balance energy efficiency with performance metrics like response time and throughput. Energy-aware resource allocation frameworks also integrate thermal and cooling management strategies to reduce data center energy footprints. Furthermore, recent advancements extend these solutions to multi-cloud, edge, and IoT environments, where adaptive and predictive allocation mechanisms enhance sustainability and operational efficiency across distributed infrastructures.