Using Digital Twins in the Development of Complex Dependable Real-Time Embedded Systems

Abstract

Modelling execution times in complex real-time embedded systems is vital for understanding and predicting tasks' temporal behaviour, and to improve the system scheduling performance. Previous research mainly relied on worst-case execution time estimations based on formal static analyses that are often pessimistic. The models that resulted are hard to maintain and even harder to validate. In this work, the novel use of Digital Twins provides opportunities to improve this issue and beyond for dependable real-time systems. We aim to establish and contribute to three questions: (i) how to easily model execution times with an adequate level of abstraction, and how to evaluate the quality of that model; (ii) how to identify errors in the models and how to evaluate the impact of errors; and (iii) how to make decisions as to when and how to improve the models. In this paper, we proposed a Digital Twin-based adaptation framework, and demonstrated its use for modelling and refining execution time profiles. Key decisions concerning the quality of the model and its impact on performance are evaluated. Finally, some challenges and key research questions for the formal method community are proposed.

Reducing Loss of Service for Mixed-Criticality Systems through Cache- and Stress-Aware Scheduling

“…However with time, they have the advantage of addressing concerns, principally representativity, highlighted in [19]. Refinements of the model based on observations from the actual system have been return observations 27: end function considered in [13]. We focus in the following on bootstrapping the model, in the absence of the actual system.…”