  In pervasive dynamic environments, vehicles connect to other objects to send
operational data and receive updates so that vehicular applications can provide
services to users on demand. Automotive systems should be self-adaptive,
thereby they can make real-time decisions based on changing operating
conditions. Emerging modern solutions, such as microservices could improve
self-adaptation capabilities and ensure higher levels of quality performance in
many domains. We employed a real-world automotive platform called Eclipse Kuksa
to propose a framework based on microservices architecture to enhance the
self-adaptation capabilities of automotive systems for runtime data analysis.
To evaluate the designed solution, we conducted an experiment in an automotive
laboratory setting where our solution was implemented as a microservice-based
adaptation engine and integrated with other Eclipse Kuksa components. The
results of our study indicate the importance of design trade-offs for quality
requirements' satisfaction levels of each microservices and the whole system
for the optimal performance of an adaptive system at runtime.
