Response time analysis, which determines whether timing guarantees are satisfied for a given system, has matured to industrial practice and is able to consider even complex activation patterns modelled through arrival curves or minimum distance functions. On the other side, sensitivity analysis, which determines bounds on parameter variations under which constraints are still satisfied, is largely restricted to variation of single-valued parameters as e.g. task periods. In this paper we provide a sensitivity analysis to determine the bounds on the admissible activation pattern of a task, modelled through a minimum distance function. In an evaluation on a set of synthetic testcases we show, that the proposed algorithm provides significantly tighter bounds, than previous exact analyses, that determine allowable parametrizations of activation patterns.

Integrating applications with different safety requirements on a common platform requires either certification of all applications to the highest safety level or "sufficient independence" among them. As the former typically is too costly, isolation mechanisms, such as monitoring, are key in the design of mixed-criticality systems. We regard monitoring of activation patterns of real-time applications in mixed-criticality systems. Existing solutions monitor single tasks in isolation. We present a monitoring scheme which allows to monitor groups of tasks jointly. It allows to express correlations between activations and provides improved resource utilization as no isolation between tasks in a group is enforced.

Model-based verification of timing properties has become industrial practice in design processes of safety-critical hard real-time systems. To validate the correctness of the used verification model, systems are additionally monitored during regular operation. With a growing variety of activation patterns considered in verification, some of them with infinite range capturing arbitrary activation patterns, the known approaches to monitoring, which assume periodic streams, have become inapplicable or they suffer from large overhead due to piecewise continuous time monitoring. In this paper we present a light-weight monitoring approach for arbitrary activation patterns. It profits from the discrete time property of a minimum distance event representation which is used instead of the continuous time representation used in earlier approaches. The method has a configurable constant runtime overhead in terms of memory and computation and allows conservative monitoring of a given arbitrary minimum distance function. Furthermore, we provide conditions under which the monitoring function is exact.