Design and Development of a Machine Learning-Based Task Orchestrator for Intelligent Systems on Edge Networks

This paper proposes an edge-centric workload orchestration approach that uses machine learning in a three-tier vehicular architecture (edge, cloud via roadside unit, or cloud via cellular base station). The orchestrator uses a wireless network at the edge to receive and send over-the-air requests from vehicles, considering a metropolitan network connects the entire edge structure to support the high mobility of devices, allowing vehicles to share information and resources. Additionally, suppose the edge is congested, or its resources are unavailable. In that case, cloud resources will be used via roadside or cellular networks using a wide-area network to meet the tasks' time constraints. Moreover, the proposed machine learning model uses variance-based sensitivity analysis to determine which inputs influence the model's final decision. The experiments performed on the EdgeCloudSim simulator are based on modelling computational and network resources besides the representation of vehicles. The results indicate that our approach best fits task offloading over-the-air, outperforming the comparative experiments between the one-stage(our approach) model against two-stage and random models. Furthermore, by using our one-stage model that outputs the average of the prediction interval and the variance of this interval, we can measure how confident our model is in its prediction.