A growing number of networked applications is implemented on increasingly complex automotive platforms with several bus standards and gateways. Together, they challenge the automotive design process. Recent automotive software standards, in particular AUTOSAR that defines a network runtime environment on top of the existing automotive standards, are intended to improve portability and interoperability. AUTOSAR shall replace or extend earlier proprietary software architecture solutions, but it does not yet sufficiently address time and platform modeling and specification. The presentation will give some examples for open issues with respect to performance, timing and interoperability. It will show how recent results in compositional performance analysis can be exploited to analyze such networked systems, and how to apply design space exploration in a complex automotive supply chain. The resulting tools and methods can even be used to optimize the robustness of an architecture which is important to handle updates and extend the lifetime of an architecture

Design space exploration of embedded systems typically focuses on classical design goals such as cost, timing, buffer sizes, and power consumption. Robustness criteria, i.e. sensitivity of the system to variations of properties like execution and transmission delays, input data rates, CPU clock rates, etc., has found less attention despite its practical relevance.

System level performance analysis plays a fundamental role in the design process of real-time embedded systems. Several different approaches have been presented so far to address the problem of accurate performance analysis of distributed embedded systems in early design stages. The existing formal analysis methods are based on essentially different concepts of abstraction. However, the influence of these different models on the accuracy of the system analysis is widely unknown, as a direct comparison of performance analysis methods has not been considered so far. We define a set of benchmarks aimed at the evaluation of performance analysis techniques for distributed systems. We apply different analysis methods to the benchmarks and compare the results obtained in terms of accuracy and analysis times, highlighting the specific effects of the various abstractions. We also point out several pitfalls for the analysis accuracy of single approaches and investigate the reasons for pessimistic performance predictions.

Compositional performance analysis iteratively alternates local scheduling analysis techniques and output event model propagation between system components to enable performance analysis of heterogeneous distributed systems. In spite of its high scalability and adaptability, the compositional approach may suffer from overestimated results compared with other system performance verification techniques. The main reason is an incomplete consideration of event sequence correlations. In this paper we present a new technique that improves the output jitter calculation by correlating jitter and response times and offers significantly tighter analysis bounds.

Embedded system optimization typically considers objectives such as cost, timing, buffer sizes, and power consumption. Robustness criteria, i.e. sensitivity of the system to property variations like execution and transmission delays, input data rates, CPU clock rates, etc., has found less attention despite its practical relevance. In this paper we present an approach for optimizing multidimensional robustness criteria in complex distributed embedded systems. The key novelty of our approach is a scalable stochastic multi-dimensional sensitivity analysis technique approximating the sought-after sensitivity front from two sides, i.e. coming from the space of working and from the space of non-working system property combinations. We utilize the proposed stochastic sensitivity analysis to derive multi-dimensional robustness metrics, which are capable of bounding the robustness of given system configurations with little computational effort. The proposed metrics can significantly speed up multidimensional robustness optimization by quickly identifying promising system configurations, whose in-depth robustness evaluation can be performed subsequently to the optimization process

Dynamic voltagescaling and sleep state control have been shown to be extremely effective in reducing energy consumption in CMOS circuits. Though plenty of research papers have studied the application of these techniques in real-time embedded system design through intelligent task and/or voltage scheduling, most of these results are limited to relatively simple real-time application models. In this paper, a comprehensive real-time application model including periodic, sporadic and bursty tasks as well as distributed real-time constraints such as end-to-end delays is considered. Two methods are presented for reducing energy consumption while satisfying complex real-time constraints for this model. Experimental results show that the methods achieve significant energy savings without violating any deadlines.

System robustness is a major concern in the design of efficient and reliable state-of-the-art heterogenous embedded real-time systems. Due to complex component interactions, resource sharing and functional dependencies, one-dimensional sensitivity analysis cannot cover all effects that modifications of one system property may have on system performance. One reason is that the variation of one property can also affect the values of other system properties requiring new approaches to keep track of simultaneous parameter changes. In this paper we present a heuristic and a stochastic approach suited for the multi-dimensional sensitivity analysis of large heterogenous embedded systems with complex timing constraints. 1

Jitter is a critical problem for the design of both distributed embedded systems and real-time control systems. This work considers meeting the completion jitter constraints of a set of independent, periodic, hard real-time tasks scheduled according to a preemptive fixed-priority scheme. Control over completion jitter is achieved by judiciously applying dynamic voltage scaling (DVS). Through simulation, the proposed method is shown to be an effective tool to meet jitter constraints on a variety of systems.

During real-world design of embedded real-time systems, it cannot be expected that all performance data required for scheduling analysis is fully available up front. In such situations, sensitivity analysis is a promising approach to deal with uncertainties that result from incomplete specifications, early performance estimates, late feature requests, and so on. Sensitivity analysis allows the system designer to keep track of the flexibility of the system, and thus to quickly assess the impact of changes of individual hardware and software components on system performance. In this paper we integrate sensitivity analysis into our system-level performance analysis framework SymTA/S and show its benefits during the design of complex, networked multiprocessor embedded real-time systems.

Today, only very few techniques out of the host of work on formal performance and timing analysis have been adopted in MpSoC (multiprocessor system-on-chip) design. One of the key reasons is a mismatch between the scheduling models assumed in most formal approaches and the heterogeneous world of MpSoC scheduling techniques and communication patterns. This heterogeneity results from IP reuse and a plug-and-play design style, required to effectively reach the necessary design productivity. A second problem is the model complexity. While complex, specialized models can find their way into industry niches, their broad acceptance is extremely doubtful. In this paper, we review the existing scheduling analysis techniques with respect to these key requirements and derive a good compromise between model simplicity on the one hand, and applicability to MpSoC design on the other hand. The approach represents system-level scheduling analysis as a flow-analysis problem for event streams that can be configured to reuse the existing local scheduling analysis techniques. We define transformations between few key event stream models to meet the interfacing requirements of the compositional design style. An example demonstrates the application of the approach, as well as the worthiness of the results.