Price-Based Task Offloading for Load-Imbalance Vehicular Multi -Access Edge Computing
Jindou Xie, F. Yifeng Zheng, Wanli Wen, Yunjian Jia · 2024
This paper explores task offloading within load-imbalance vehicular multi-access edge computing (MEC) sys-tems. Addressing the uneven distribution of mobile vehicles causing road side unit (RSU) load imbalances, we leverage vehicle mobility and service pricing to redistribute task loads. RSUs strategically set service prices to alleviate congestion and enhance profitability. Meanwhile, vehicles assess these prices to determine task offloading to different RSU s while in motion, to maximize their individual utility. To achieve this, the Karush- Kuhn- Tucker (KKT) condition is applied to determine the optimal RSU ser-vice pricing. Furthermore, a multi-agent reinforcement learning algorithm, Nash Q-Iearning, is utilized to manage the vehicles' offloading decisions. Simulation results substantiate the efficacy of the Nash Q-Iearning-based task offloading scheme, enhancing the utility of mobile vehicles within competitive environments.