Improving bigdata 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: EcoDriving, 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 streaming application can be modeled as a directed graph where vertices are operators instances and edges are data streams (i.e., continuous series of tuples, generated by an operator). The central challenge when developing streaming applications is the way to assign operators to given resources for optimal performances (i.e., resource allocation). We showed that straightforward allocation, when based on intrinsic data parallelism, do not yield satisfying results. We developed a novel approach that takes into account the specifics of the target application, on the one hand, and on the other hand the systems performance metrics we derived from the infrastructure. The proposed approach improves the throughput by 4% compared to the previous approach. In the automotive context, this number translates into 800k additional vehicles to be served by the infrastructure over the 20M of the expected number of connected vehicles by Groupe PSA.

A Databased Approach to Predict the Response Time of Cloudbased Vehicle Functions Future vehicular applications require sufficient computing and storage resources. Actual onboard resources are limited and inadequate to deal with steadily increasing requirements. In order to overcome the computational limitation in vehicles, computing intensive functions can be offloaded to a cloud. The socalled cloudbased vehicle functions run in the cloud and utilize cloud capability instead of vehicleu0027s onboard resources. The suitability of the vehicle functions for outsourcing to the cloud should be ensured in an early stage of development. Otherwise, shifting improper functions can lead to serious consequences for the vehicle control system. By the suitability analysis several criteria like functional safety, data dependency and response time should be considered. In this paper, we focus on the criterion response time and present a framework to predict the response time along a sample route.