Research on QoS-aware Resource Scaling in Cloud Computing focuses on designing intelligent and adaptive mechanisms to dynamically adjust cloud resources while ensuring that Quality of Service (QoS) requirements—such as latency, throughput, reliability, and availability—are consistently met. This area explores strategies for automatic scaling of computational, storage, and network resources in response to fluctuating workloads and user demands. Key research directions include predictive resource provisioning using machine learning, SLA-aware scaling policies, and adaptive load balancing that accounts for QoS metrics. Other emerging topics involve energy-efficient and cost-aware scaling, cloud–edge integrated QoS management, multi-objective optimization for balancing performance and resource utilization, and the use of reinforcement learning for real-time scaling decisions. Additionally, research on fault-tolerant and self-healing resource management, containerized and serverless architectures for elastic QoS-aware deployment, and intelligent orchestration frameworks for heterogeneous cloud environments are promising avenues for advancing scalable and reliable cloud services.