At first glance, running mission-critical applications at the edge appears to be an opportunity to benefit from scalability and reusability. The low latency to the edge makes it particularly interesting for mission-critical applications. The hardware heterogeneity of the edge, coupled with the strict requirement for the execution time of a mission-critical application, creates the need for flexible application control and, at the same time, increases the complexity of modeling such systems. With its Feature Models (FMs), software product line engineering offers a modeling option for various alternative compositions of an application. However, the calculation of valid configurations takes too long for the dynamic adaptation of an application flow of a mission-critical application. This paper presents an approach for slicing FMs to support mission-critical applications. Our approach supports the strict requirements on the execution time of mission-critical applications.

Mission-critical applications, which must adhere to processing deadlines, can benefit from low latencies offered by the edge. Adapting the Quality of Result allows for targeted processing times by selecting various approximations. Feature models can be employed to manage the resulting multitude of possible configurations. However, the deployment and (re)-configuration process is very time-consuming, making it impractical for mission-critical applications. In this work, we introduce a processing pipeline with components that significantly accelerate online (re)-configuration based on changing latencies compared to the state-of-the-art. Additionally, we address the edge-specific discovery of potential microservice chains capable of executing the application.