An Adaptive Task Offloading Strategy for Edge Computing based on Dynamic Network State

Abstract

Dynamic edge networks suffer from fluctuating bandwidth, latency, and edge-node load, which weaken conventional task offloading strategies under deadline and energy constraints. This study proposes an adaptive task offloading strategy based on dynamic network states. An online broad learning system predicts short-term bandwidth, transmission delay, and load from sliding-window observations. The predictions are embedded into a model predictive control framework for rolling optimization of the local-edge task allocation ratio, while confidence-interval-guided compensation adjusts decisions under prediction uncertainty. ADMM is used for distributed solution. NS-3/Python simulations show that the proposed method achieves a $93\%$ task success rate, 1.22 W average terminal energy consumption, 3.2 switches/s, and 45 ms average completion time, outperforming local execution, reactive offloading, and DQN-based offloading. The proposed strategy improves real-time reliability, energy efficiency, and robustness in highly dynamic edge computing environments.