Low Complexity and Mobility-Aware Robust Radio, Storage, Computing, and Cost Management for Cellular Vehicular Networks

In this paper, a novel two-time scale task offloading/delivery framework is presented which dynamically manages radio, storage, computing, and cost aspects through multiple Mobile Edge Computing (MEC) servers distributed across Road Side Units (RSUs), Base Stations (BSs), vehicles, and pedestrians. To enhance flexibility, we introduce a smart dynamic task partitioning and cooperative offloading/delivery mechanism, enabling users to offload task partitions to various MEC servers. Uncertainties stemming from users' high mobility and traffic variations may lead to unpredictable task offloading demands and Imperfect Channel State Information (I-CSI), potentially causing unsuccessful task offloading/delivery. In response, we propose a robust and cooperative two-time-scale task offloading/delivery approach, adept at handling demand uncertainty and I-CSI. The problem formulation involves a two-time scale Markov decision process, aimed at minimizing task offloading/delivery costs while maximizing task completion rates. Large time-scale slots incorporate intelligent RSU activation/deactivation, while short time-scale slots focus on task offloading/delivery. To efficiently implement this framework, we propose Federated Reinforcement Learning (FRL) algorithm based on Deep Deterministic Policy Gradient (DDPG); namely Low overhead Multi-Agent DDPG (LoMADDPG) where at each slot, all agents tune their Actor and Critic neural network weights with the help of Global Software-defined Networking (SDN) Controller (GSC) which acts as a global weight tuner. Simulation results illustrate that our proposed task offloading/delivery scheme can result in significant improvement in task completion rate and cost reduction.