Workload allocation in edge computing has become an important research direction, aiming to efficiently distribute computational tasks across resource-constrained and heterogeneous edge, fog, and cloud infrastructures. Research papers in this area investigate strategies to minimize latency, reduce energy consumption, and improve quality of service (QoS) for delay-sensitive applications such as autonomous driving, real-time video analytics, industrial IoT, and smart healthcare. Studies explore static, dynamic, and hybrid workload allocation models, considering factors like user mobility, fluctuating workloads, network bandwidth, and edge server capacity. Recent works leverage optimization algorithms, game theory, and deep reinforcement learning to enable adaptive workload allocation that balances performance, fairness, and resource utilization. Security-aware and privacy-preserving workload allocation frameworks are also gaining attention to protect sensitive data during task offloading and execution. Moreover, collaborative workload allocation across multi-access edge computing (MEC), fog, and cloud layers is being studied to enhance scalability and resilience. Overall, workload allocation research highlights the need for intelligent, context-aware, and secure mechanisms that ensure efficient utilization of edge resources while delivering real-time and reliable services in highly dynamic environments.