In this paper, we propose a simulation-based methodology for worst-case response time estimation of distributed realtime systems. Schedulability analysis produces pessimistic upper bounds on process response times. Consequently, such an analysis can lead to overdesigned systems resulting in unnecessarily increased costs. Simulations, if well conducted, can lead to tight lower bounds on worst-case response times, which can be an essential input at design time. Moreover, such a simulation methodology is very important in situations when the running application or the underlying platform is such that no formal timing analysis is available. Another important application of the proposed simulation environment is the validation of formal analysis approaches, by estimating their degree of pessimism. We have performed such an estimation of pessimism for two responsetime analysis approaches for distributed embedded systems based on two of the most important automotive communication protocols: CAN and FlexRay.
Abstract-Today's embedded systems are typically exposed to varying load, due to e.g. changing number of tasks and variable task execution times. At the same time, many of the most frequent real-life applications are not characterized by hard real-time constraints and their design goal is not to satisfy certain hard deadlines in the worst case. Moreover, from the user's perspective, achieving a high level of processor utilization is also not a primary goal. What the user needs, is to exploit the available resources (in our case processor time) such that a high level of quality of service (QoS) is delivered. In this paper we propose efficient run-time approaches, able to distribute the processor bandwidth such that the global QoS produced by a set of applications is maximized, in the context in which the processor demand from individual tasks is continuously varying. Extensive experiments demonstrate the efficiency of the proposed approaches.
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