Recent research in QoS-aware resource scaling in cloud computing emphasizes developing intelligent and adaptive mechanisms that dynamically adjust computing resources to maintain consistent service quality under varying workloads. These approaches integrate predictive models, real-time monitoring, and feedback control systems to ensure that critical Quality of Service (QoS) parameters such as latency, throughput, and availability are continuously met. By applying techniques like machine learning, reinforcement learning, and rule-based auto-scaling, modern frameworks can proactively scale resources both vertically and horizontally, minimizing SLA violations while optimizing cost and energy usage. Overall, QoS-aware scaling enhances system reliability, performance stability, and user satisfaction in dynamic and heterogeneous cloud environments.