No abstract

No abstract

No abstract

Abstract. Embedded systems increasingly encompass both dependability and responsiveness requirements. While sophisticated techniques exist, on a discrete basis, for both dependability/fault-tolerance (FT) and real-time (RT), the composite considerations for FT+RT are still evolving. Obviously the different objectives needed for FT and RT make composite optimization hard. In this paper, the proposed Multi Variable Optimization (MVO) process develops integrated FT+RT considerations. We introduce dependability as an initial optimization criteria by confining error propagation probability, i.e., limiting the interactions. Subsequently, quantification of interactions together with RT optimization by minimizing scheduling length is developed. A simulated annealing approach is utilized to find optimized solutions. We provide experimental results for our approach, showing significant design improvements over contemporary analytical initial feasibility solutions.

Moving from the traditional federated design paradigm, integration of mixedcriticality software components onto common computing platforms is increasingly being adopted by automotive, avionics and the control industry. This method faces new challenges such as the integration of varied functionalities (dependability, responsiveness, power consumption, etc.) under platform resource constraints and the prevention of error propagation. Based on model driven architecture and platform based design's principles, we present a systematic mapping process for such integration adhering a transformation based design methodology. Our aim is to convert/transform initial platform independent application specifications into post integration platform specific models. In this paper, a heuristic based resource allocation approach is depicted for the consolidated mapping of safety critical and non-safety critical applications onto a common computing platform meeting particularly dependability/fault-tolerance and real-time requirements. We develop a supporting tool suite for the proposed framework, where VIATRA (VIsual Automated model TRAnsformations) This work has been partly supported by the EU IST FP6 DECOS. 246 S. Islam et al.is used as a transformation tool at different design steps. We validate the process and provide experimental results to show the effectiveness, performance and robustness of the approach.

No abstract

a b s t r a c tEmbedded systems increasingly entail complex issues of hardware-software (HW-SW) co-design. As the number and range of SW functional components typically exceed the finite HW resources, a common approach is that of resource sharing (i.e., the deployment of diverse SW functionalities onto the same HW resources). Consequently, to result in a meaningful co-design solution, one needs to factor the issues of processing capability, power, communication bandwidth, precedence relations, real-time deadlines, space, and cost. As SW functions of diverse criticality (e.g. brake control and infotainment functions) get integrated, an explicit integration requirement need is to carefully plan resource sharing such that faults in low-criticality functions do not affect higher-criticality functions.On this background, the main contribution of this paper is a dependability-driven framework that helps to conduct the integration of SW components onto HW resources such that the maintenance of system dependability over integration of diverse criticality components is assured by design.We first develop a clustering strategy for SW components into Fault Containment Modules (FCMs) such that error propagation via interaction is minimized. Subsequently, the rules of composition for FCMs with respect to error propagation are developed. To allocate the resulting FCMs to the existing HW resources we provide several heuristics, each optimizing particular attributes thereof. Further, a framework for assessing the goodness of the achieved HW-SW composition as a dependable embedded system is presented. Two new techniques for quantifying the goodness of the proposed mappings are introduced by examples, both based on a multi-criteria decision theoretic approach.

No abstract