A Reliable Edge Server Deployment Algorithm Based on Spectral Clustering and a Deep Q-network Strategy using Multi-objective Optimization

Mobile edge computing (MEC) enables real-time processing and reduces core network congestion by bringing computing resources closer to data sources. However, optimizing edge server deployments to minimize latency, energy consumption, and load imbalance remains challenging. We propose the SC-DQN strategy, combining spectral clustering and deep Q-network (DQN) techniques. Spectral clustering analyzes the spatial distribution of base stations, grouping them and identifying cluster centers as initial deployment sites. A deep reinforcement learning environment then enables each edge server (agent) to iteratively optimize deployment, minimizing latency, energy consumption, and load imbalance. Experiments with Shanghai Telecom data demonstrate that SC-DQN improves load balancing by 23.05%, reduces latency by 45.32%, and lowers energy consumption by 9.64%, outperforming traditional methods.