Online Adaptive Resource Management With Stability Guarantees in Collaborative Edge Environments

Objectives In rapidly evolving industrial environments, resource management in Mobile Edge Computing (MEC) has gained increasing attention, aiming to ensure Quality of Service (QoS) for Artificial Intelligence of Things (AIoT) applications. While MEC reduces end‐to‐end delay, tasks offloaded to the cloud still encounter bottlenecks when processing massive AIoT‐generated data streams. To overcome this, we introduce a Collaborative Edge‐Edge (CE2) architecture that integrates heterogeneous edge servers and devices, enabling real‐time latency‐energy trade‐offs and accelerating AI‐driven decision‐making at the network edge. Managing resources in such dynamic, multi‐task, multi‐server environments remains challenging, especially under variable task‐arrival rates. Methods To tackle this, we propose LyDRM, a hybrid dynamic resource management scheme that synergistically combines model‐based optimization with model‐free deep reinforcement learning (DRL). A Lyapunov optimization module is embedded to enforce queue‐stability constraints, ensuring bounded task backlogs over time. Result To validate its effectiveness, extensive simulations show that LyDRM reduces the average weighted system cost‐defined as a combination of latency and energy metrics‐by at least 39.89%, significantly lowers both latency and energy consumption, accelerates convergence, and maintains long‐term stability in dynamic AIoT scenarios.