Multi-Layer Offloading at the Edge for Vehicular Networks

This paper proposes a multi-layer platform for job offloading in vehicular networks. Offloading is performed from vehicles in the Vehicular Domain towards Multi-Access Edge Computing (MEC) Servers deployed at the edge of the network, and between MEC Servers. Offloading decisions at both domains are challenging for the overall system performance. Optimization at the MEC Layer domain is obtained by model-based Reinforcement Learning, while a strategy to decide the best offloading rate from the Vehicular Domain is defined to achieve the desired trade-off between costs and performance. Numerical analysis shows the achieved performance.