While there has been considerable work on selfadaptive systems, applying these techniques to networked, embedded systems poses several new problems due to the requirements of embedded real-time systems. Among others, we have to consider memory and hardware limitations, as well as task schedulability and timing dependencies. The goal of this paper is to find a correct placement of software components efficiently, even though most of these individual constraints are highly intractable (NP-complete). This is a prerequisite for runtime adaptation in such domains and can be used for system optimization, extension or failure handling.We introduce an integrated model of system constraints for efficient computation of software component allocation, focusing on automotive embedded systems. For solving these, we have developed and compared two techniques based on SAT solving and Simulated Annealing, which enforce placement constraints efficiently. This reduces the size of the constraints significantly, but still leads to 2 million variables and more than 126 thousand equations in our case study with realistic automotive system settings. We show that both approaches provide solutions in several seconds on current commodity hardware, and show that SAT solving is more efficient for larger sets of equations.

Modern distributed embedded systems are reaching an extreme complexity which is very hard to master with traditional methods. Particularly the need for these systems to adapt their behavior autonomously at runtime to changing conditions is a demanding challenge. Since most industrial application domains of distributed embedded systems have high demands on reliability and safety, we need a dependable self-adaptation mechanism to apply adaptation successfully in these domains. Therefore, we propose a concept to guarantee the proper system behavior and a mechanism which preserves the predefined functional and non-functional requirements of the system

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Future safety-critical systems will be highly automated or even autonomous and they will dynamically cooperate with other systems as part of a comprehensive ecosystem. This together with increasing utilization of artificial intelligence introduces uncertainties on different levels, which detriment the application of established safety engineering methods and standards. These uncertainties might be tackled by making systems safety-aware and enabling them to manage themselves accordingly. This paper introduces a corresponding conceptual dynamic safety management framework incorporating monitoring facilities and runtime safety-models to create safety-awareness. Based on this, planning and execution of safe system optimizations can be carried out by means of self-adaptation. We illustrate our approach by applying it for the dynamic safety assurance of a single car.

Today’s vehicles are evolving towards smart cars, which will be able to drive autonomously and adapt to changing contexts. Incorporating self-adaptation in these cyber-physical systems (CPS) promises great benefits, like cheaper software based redundancy or optimised resource utilisation. As promising as these advantages are, a respective proportion of a vehicle’s functionality poses as safety hazards when confronted with faultand failure situations. Consequently, a system’s safety has to been sured with respect to the availability of multiple software applications, thus often resulting in redundant hardware resources, such as dedicated backup control units. To benefit from self-adaptation by means of creating efficient and safe systems, this work introduces a safety concept in form of a generic adaptation mechanism (GAM). In detail, this generic adaptation mechanism is introduced and analysed with respect to generally known and newly created safety hazards, in order to determine a minimal set of system properties and architectural limitations required to safely perform adaptation. Moreover, the approach is applied to the ICT architecture of a smart e-car, thereby highlighting the soundness, general applicability, and advantages of this safety concept and forming the foundation for the currently ongoing implementation of the GAM within a real prototype vehicle