Research on QoS-aware Resource Allocation in Cloud Computing focuses on developing intelligent strategies to optimize system performance, energy efficiency, and user satisfaction while meeting quality of service requirements. Recent studies highlight hybrid approaches that combine heuristics like Heterogeneous Earliest Finish Time (HEFT) with optimization algorithms such as Whale Optimization and Ant Colony Optimization to minimize task completion time, execution cost, and balance load across resources. Energy-efficient virtual machine placement strategies further enhance QoS by preventing overloads, improving resource utilization, and reducing operational costs. These advancements collectively emphasize the importance of integrating QoS considerations into cloud resource allocation frameworks to achieve efficient, reliable, and cost-effective cloud services.