An Online Computation Offloading Approach With Dual Stability Guarantee for Heterogeneous Tasks in MEC-Enabled IIoT
Kai Ping Peng, Chengfang Ling, Shangguang Wang, Victor C. M. Leung · IEEE Transactions on Mobile Computing · 2025
With the explosive growth of information and the continuous expansion of applications, Industrial Internet of Things (IIoT) is facing huge data processing and storage pressure. With mobile edge computing (MEC) technology, the computing power network connects the geographically distributed computing nodes and then coordinates the allocation and scheduling of resources, transmits data, and eventually relieves the pressure of the industrial site. However, the rigorous demands of IIoT for real-time and stability pose some daunting challenges. To this end, we propose an online computation offloading approach with dual stability guarantee, named OCODSG. Specifically, the Lyapunov function is used to optimize the stability of the virtual queue, and the system stability is optimized based on the network jitter measurement. Moreover, the Dueling Double Deep Q Network (D3QN) algorithm based on deep reinforcement learning (DRL) is used for model autonomous training, while Gaussian noise is added to the network parameter space to encourage exploration and enhance algorithm robustness. Finally, experimental results on both simulated and real datasets demonstrate that OCODSG improves service efficiency and system stability.