Kuksa*: Self-Adaptive Microservices in Automotive Systems

TL;DR

This paper presents a microservices-based framework for self-adaptive automotive systems, enabling real-time decision-making and improved performance in dynamic environments using the Eclipse Kuksa platform.

Contribution

It introduces a novel microservices architecture framework for enhancing self-adaptation in automotive systems, validated through real-world experiments.

Findings

Design trade-offs impact quality satisfaction levels.

Microservice-based adaptation engine improves system responsiveness.

Optimal performance depends on balancing system components.

Abstract

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.