Modern automotive software components are often first developed by different suppliers and then integrated under limited resources by a manufacturer. The integration of software components under various resource configurations is prone to timing errors because the components are resources independently designed by the supplier and viewed by the manufacturer as black boxes during the integration stage, so that imposing resource constraints/requirements on their behavior is a challenge. This paper introduces an engineering awareness environment for the analysis of automotive systems with respect to two perspectives: 1) time-aware design models that correspond to the supplier perspective; and 2) resource-aware design models imposed by the manufacturer during integration. To this end, first we propose two timed behavioral models, a time-constrained model (TcM) and a resource-constrained model (RcM) that are extended from a functional model (FM). A timing analysis of applications can hence be conducted incrementally by adopting the separation of concerns principle coming from the model-driven architectures (MDAs). Second, given a basic application component description of AUTomotive Open System Architecture with timing properties, we specify how to define the behavior of the basic components as process terms using a process algebra, algebra of communicating shared resources with value passing (ACSR-VP), in order to exploit the description capability of the language for both timing aspects and resource-constrained aspects of a system. As a result, a timed behavioral model of a system can be seamlessly refined by various resource configurations, and both platform-independent and platform-dependent timing properties of real-time systems can be analyzed in a consistent and efficient manner.

This paper introduces a compositional framework for analyzing the predictability of component-based embedded real-time systems. The framework utilizes automated analysis of tasks and communication architdepicts the structureectures to provide insight on the schedulability and data flow. The communicating tasks are gathered within components, making the system architecture hierarchical. The system model is given by a set of Parameterized Stopwatch Automata modeling the behavior and dependency of tasks, while we use Uppaal to analyze the predictability. Thanks to the Uppaal language, our model-based framework allows expressive modeling of the behavior. Moreover, our reconfigurable framework is customizable and scalable due to the compositional analysis. The analysis time and cost benefits of our framework are discussed through an avionic case study.

Compositional reasoning on hierarchical scheduling systems is a well-founded formal method that can construct schedulable and optimal system configurations in a compositional way. However, a compositional framework formulates the resource requirement of a component, called an interface, by assuming that a resource is always supplied by the parent components in the most pessimistic way. For this reason, the component interface demands more resources than the amount of resources that are really sufficient to satisfy sub-components. We provide two new supply bound functions which provides tighter bounds on the resource requirements of individual components. The tighter bounds are calculated by using more information about the scheduling system.

We evaluate our new tighter bounds by using a model-based schedulability framework for hierarchical scheduling systems realized as Uppaal models. The timed models are checked using model checking tools Uppaal and Uppaal SMC, and we compare our results with the state of the art tool CARTS.

This paper contains two contributions: 1) A development methodology involving two techniques to enhance the resource utilization and 2) a new generic multi-core resource model for hierarchical scheduling systems.

As the first contribution, we propose a two-stage development methodology relying on the adjustment of timing attributes in the detailed models during the design stage. We use a lightweight method (statistical model checking) for design exploration, easily assuring high confidence in the correctness of the models. Once a satisfactory design has been found, it can be proved schedulable using the computation costly method (symbolic model checking). In order to analyze a hierarchical scheduling system compositionally, we introduce the notion of a stochastic supplier modeling the supply of resources from each component to its child components in the hierarchy. We specifically investigate two different techniques to widen the set of provably schedulable systems: 1) a new supplier model; 2) restricting the potential task offsets.

We also provide a way to estimate the minimum resource supply (budget) that a component is required to provide. In contrast to analytical methods, we prove non-schedulable cases via concrete counterexamples. By having richer and more detailed scheduling models this framework, has the potential to prove the schedulability of more systems.

As the second contribution, we introduce a generic resource model for multi-core hierarchical scheduling systems, and show how it can be instantiated for classical resource models: Periodic Resource Models (PRM) and Explicit Deadline Periodic (EDP) resource models. The generic multi-core resource model is presented in the context of a compositional model-based approach for schedulability analysis of hierarchical scheduling systems.

The multi-core framework presented in this paper is an extension of the single-core framework used for the analysis in the rest of the paper.

This paper presents a compositional framework for the modeling and analysis of hierarchical scheduling systems. We consider both schedulability and energy consumption of individual components, while analyzing a single core setting with a voltage frequency scaling CPU. According to the CPU frequency scaling, each task has a set of different execution times. Thus, the energy consumption of the whole system varies from one execution to another. We analyze each component individually by checking the feasibility of its workload against both the CPU availability and energy consumption constraints of such a component. Our periodic task model considers both static and dynamic priorities together with preemptive and non-preemptive behaviors. The models are realized using different forms of Hybrid Automata, all of which are analyzed using variants of UPPAAL. The CPU frequencies, task behavior and scheduling policies used in each component are some of the reconfigurable parameters of the system. Finally, we demonstrate the applicability and scalability of our framework by analyzing the schedulability and power consumption of an avionics system. (C) 2015 Elsevier B.V. All rights reserved.