Deep Reinforcement Learning Edge Workload Orchestrator for Vehicular Edge Computing

Smart vehicles in Vehicular Edge Computing Environments run latency sensitive applications, such as driver assistance, autonomous driving, accident prevention and others that require quick response times due to low latency constraints. This work focus on the workload orchestration and the decision to offload vehicular application tasks from vehicles to the network edge to increase computing powers and minimize latency. We introduce a new offloading orchestration algorithm based on Deep Reinforcement Learning. We show that the proposed algorithm has a lower task failure rate than the best solutions from the literature, while requiring lower computational power.