Cloud - Edge Offloading Model for Vehicular Traffic Analysis

The proliferation of smart sensing and computing devices, capable of collecting a vast amount of data, has made the gathering of the necessary vehicular traffic data relatively easy. However, the analysis of these big data sets requires computational resources, which are currently provided by the Cloud Data Centers. Nevertheless, the Cloud Data Centers can have unacceptably high latency for vehicular analysis applications with strict time requirements. The recent introduction of the Edge computing paradigm, as an extension of the Cloud services, has partially moved the processing of big data closer to the data sources, thus addressing this issue. Unfortunately, this unlocked multiple challenges related to resources management. Therefore, we present a model for scheduling of vehicular traffic analysis applications with partial task offloading across the Cloud - Edge continuum. The approach represents the traffic applications as a set of interconnected tasks composed into a workflow that can be partially offloaded to the Edge. We evaluated the approach through a simulated Cloud - Edge environment that considers two representative vehicular traffic applications with a focus on video stream analysis. Our results show that the presented approach reduces the application response time up to eight times while improving energy efficiency by a factor of four.