Modeling reflex-healing autonomy for large-scale embedded systems

High-energy physics experiments require an extraordinary amount of real-time computation, and the computers implementing the online data processing must be very reliable because of the large cost associated with operating the facilities and the potential for loss of irreplaceable data. Conventional redundancy-based fault tolerance and adaptive approaches are not appropriate because of the tremendous system cost (fault tolerance is limited to a maximum of 10% overhead). In this work, we developed a framework for building robust embedded systems, which utilizes an autonomic reflex-healing approach to achieve fault tolerance. Components of the framework implement user-defined failure adaptation strategies within the context of a large-scale embedded environment. The tools embrace a model-based approach combining design specification and code-generation for both simulation and system implementation. In this paper we present the concepts and entities of the reflex and healing framework