Self-aware and self-expressive driven fault tolerance for embedded systems

The growing complexity and size of computing systems as well as the unpredictability about changes in their deployment environment make their design increasingly challenging; especially for safety critical systems. Specifically the recognition of a fault within a system might be not only time consuming but also difficult in terms of reliability and completeness. This paper presents an approach to fault tolerance based on statistical features using the concepts of self-awareness and self-expression. These features characterize the behaviour of components, they are weighted and can be compared to measured values during runtime to characterize the well-behaviour of the system. Simulations show that this approach, used with the self-awareness and self-expression system layers, combines failure recognition and recovery with effective system design.