Improving big-data automotive applications performance through adaptive resource allocation

In automotive applications, connected vehicles (CVs) can collect various information (external temperature, speed, location, etc.) and send them to a central infrastructure for exploitation in a wide range of applications: Eco-Driving, fleet management, environmental monitoring, etc. Such applications are known to generate a massive volume of data that is processed in real or near real time (i.e., data streams) depending on the target application requirements. To handle this data volume, big data architectures, based on stream computing paradigm, are usually adopted. Within this paradigm, data are continuously processed by a set of operators (elementary operations) instances. Further, a str…