Workload-aware resource management in edge computing is a rapidly evolving research area that focuses on optimizing the allocation of heterogeneous computational, networking, and storage resources by considering the dynamic nature of application workloads. Research papers in this domain emphasize how varying workload characteristics—such as latency sensitivity, task dependency, data volume, and energy demand—can be modeled to achieve efficient scheduling and provisioning across edge, fog, and cloud layers. Studies investigate adaptive resource management strategies that respond to workload fluctuations caused by user mobility, real-time service requirements, and network congestion. Recent works apply machine learning, deep reinforcement learning, and predictive analytics to design intelligent workload-aware frameworks capable of balancing performance, energy efficiency, and cost. Furthermore, research highlights multi-objective optimization approaches to simultaneously address Quality of Service (QoS), Quality of Experience (QoE), and system reliability. Workload-aware resource management also integrates with security- and privacy-preserving mechanisms to ensure safe execution of sensitive applications such as smart healthcare, industrial IoT, and autonomous driving. Overall, this research area underscores the importance of workload-awareness as a cornerstone for building flexible, resilient, and efficient edge computing ecosystems.