QoS-aware control and monitoring in fog computing is a significant research area that ensures distributed fog infrastructures can meet diverse Quality of Service (QoS) requirements such as low latency, high reliability, efficient bandwidth usage, scalability, and energy efficiency. Research papers in this domain propose frameworks and algorithms that dynamically monitor resource availability, workload fluctuations, and user mobility while controlling task scheduling, service placement, and resource allocation to guarantee QoS. Studies highlight adaptive and context-aware approaches that integrate software-defined networking (SDN), network function virtualization (NFV), and machine learning for real-time QoS prediction, monitoring, and decision-making. Recent works investigate multi-QoS optimization models that balance latency, throughput, and energy consumption under uncertain and dynamic fog–edge–cloud environments. Security- and privacy-aware QoS monitoring is also emphasized to protect sensitive IoT data while ensuring service continuity. Applications include smart healthcare, industrial IoT, autonomous vehicles, smart grids, and augmented/virtual reality, where real-time QoS guarantees are essential. Overall, research in QoS-aware control and monitoring in fog computing provides intelligent, adaptive, and resilient mechanisms to ensure reliable performance, efficient management, and user satisfaction in next-generation distributed computing systems.