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Successful design of real-time embedded systems relies heavily on the successful satisfaction of their non-functional requirements. Model-driven engineering is a promising approach for coping with the design complexity of embedded systems. However, when it comes to modeling non-functional requirements and covering specific aspects of different domains and types of embedded systems, general modeling languages for real-time embedded systems may not be able to cover all of these aspects. One solution is to use a combination of modeling languages for modeling different non-functional requirements as is done in the definition of EAST-ADL modeling language for automotive domain. In this paper, we propose a UML-based solution, consisting of different modeling languages, to model non-functional requirements in telecommunication domain, and discuss different challenges and issues in the design of telecommunication systems that are related to these requirements.

Model Driven Engineering (MDE) and Component Based Software Development (CBSD) are promising approaches to deal with the increasing complexity of Distributed Real-Time Critical Embedded Systems. On one hand, the functionality complexity of embedded systems is rapidly growing. On the other hand, extra-functional properties (EFP) must be taken into account and resource consumption must be optimized due to limited resources. However, EFP are not independent and impact each other. This paper introduces concepts and mechanisms that allow to model security specifications and derive automatically the corresponding security implementations by transforming the original component model into a secured one taking into account sensitive data flow in the system. The resulted architecture ensures security requirements by construction and is expressed in the original meta model; therefore, it enables using the same timing analysis and synthesis as with the original component model.

Introducing security features in a system is not free and brings along its costs and impacts. Considering this fact is essential in the design of real-time embedded systems which have limited resources. To ensure correct design of these systems, it is important to also take into account impacts of security features on other non-functional requirements, such as performance and energy consumption. Therefore, it is necessary to perform trade-off analysis among non-functional requirements to establish balance among them. In this paper, we target the timing requirements of real-time embedded systems, and introduce an approach for choosing appropriate encryption algorithms at runtime, to achieve satisfaction of timing requirements in an adaptive way, by monitoring and keeping a log of their behaviors. The approach enables the system to adopt a less or more time consuming (but presumably stronger) encryption algorithm, based on the feedback on previous executions of encryption processes. This is particularly important for systems with high degree of complexity which are hard to analyze statistically. 

Design of real-time embedded systems is a complex and challenging task. Model-driven development has the potential to reduce the design complexity of real-time embedded systems by increasing the abstraction level, enabling analysis at earlier phases of development, and automatic generation of code from the models. In this context, capabilities of schedulers as part of the underlying platform play an important role. They can affect the complexity of code generators and how the model is implemented on the platform. Also, the way a scheduler monitors timing behaviors of tasks and schedules them can facilitate extraction of runtime information. This information can then be used as feedback to the original model in order to identify parts of the model that may require to be re-designed and modified. In this paper, we describe our work in providing these features by introducing a second layer scheduler on top of OSE real-time operating system’s scheduler. The approach can also contribute to the predictability of systems by bringing more awareness to the scheduler about the type of real-time tasks (i.e., periodic, sporadic, and aperiodic) that are to be scheduled, and the information that should be monitored and logged for each type.