Research on QoS-aware Control and Monitoring in Fog Computing focuses on designing frameworks and mechanisms to continuously observe, manage, and optimize the performance of fog systems while ensuring that Quality of Service (QoS) requirements—such as latency, reliability, throughput, and availability—are consistently met. This area addresses challenges posed by heterogeneous and resource-constrained fog nodes, dynamic workloads, and distributed architectures. Key research directions include real-time monitoring and adaptive control of resource allocation, workload scheduling, and service provisioning, as well as predictive analytics for proactive QoS management. Other emerging topics involve multi-objective optimization balancing QoS, energy efficiency, and cost, cloud–fog–edge integrated control frameworks, and SLA-compliant monitoring and enforcement mechanisms. Additionally, research on fault-tolerant, context-aware, and machine learning-driven QoS monitoring and control, automated anomaly detection, and self-adaptive orchestration of fog resources represents significant avenues for advancing reliable, efficient, and intelligent fog computing systems.