Proactive Spatio-Temporal Request Prediction for Replica Placement in Edge-Cloud Computing

User requests in edge computing environments are inherently decentralized and dynamic, posing significant challenges for efficient and adaptive service replica placement. To address this, we formulate the service replica placement problem in an edge-cloud collaborative environment, explicitly incorporating the spatio-temporal distribution of user requests. By capturing spatial and temporal correlations, we predict future request patterns to enable forward-looking replica placement. Given the NP-hard nature of the optimization problem, we design a deep reinforcement learning (DRL) algorithm that optimizes replica placement decisions based on predictive modeling. To validate our approach, we conduct extensive experiments on real-world datasets across two typical application scenarios—grid-based and graph-based request distributions. Experimental results show our method reduces average response latency by up to 59.6% and boosts service provider profitability by 4.85% compared to reactive and temporal-only baselines. The proposed framework provides a novel and effective solution for proactive service provisioning in edge computing environments.