Research on Placement Methods in Edge Computing focuses on designing strategies and algorithms for optimally deploying applications, services, and virtualized resources (e.g., virtual machines or containers) across geographically distributed edge nodes to meet latency, energy, and Quality of Service (QoS) requirements. This area addresses challenges such as heterogeneous resource capacities, dynamic workloads, mobility of users and devices, network constraints, and limited computational power at the edge. Key research directions include heuristic- and metaheuristic-based placement algorithms, predictive and workload-aware placement strategies, and multi-objective optimization balancing latency, energy consumption, cost, and resource utilization. Other emerging topics involve container-based and microservices-oriented placement, edge–cloud collaborative deployment frameworks, and context-aware or QoS-driven placement policies. Additionally, research on adaptive, fault-tolerant, and machine learning–enhanced placement methods, as well as placement for latency-sensitive, real-time, or IoT applications, represents significant avenues for advancing efficient, intelligent, and scalable edge computing systems.