Safety-Feature Modeling and Adaptive Resource Management for Mixed-Criticality Cyber-Physical Systems

Abstract:

The project is a collaborative effort between the University of Pennsylvania and Washington University, St. Louis. The project has started in September 2013. The project is concerned with ensuring operational safety of complex cyber-physical systems such as automobiles, aircraft, and medical devices. Modern development techniques for such systems rely on independent implementation of safety features in software and subsequent integration of these features within system platform architectures. The current trend in developing these systems, driven by the need to reduce cost and energy consumption, is to share computational resources between different features. However, such sharing raises concerns about unintended interactions between the features. In addition, cyber-physical features can interact via the physical parts of the system, where operation of one feature affects physical parameters of the system upon which another feature depends. The goal of this proposal is to develop techniques to predict possible interactions between features, detect them in the features' concrete implementations, and either eliminate or mitigate these interactions through precise modeling and enforcement of mixed-criticality cyber-physical system semantics. The project aims at developing a novel framework for reasoning about and managing feature interactions in cyber-physical systems, which encompasses interactions within software, interactions through the physical dynamics of the system, and interactions via shared computational resources. The approach consists of three tightly coupled research thrusts: 1) a novel way of modeling features as automata equipped with both physical dynamics of the feature environment, and an assigned criticality level in each state of an automaton, 2) new automata-theoretic and control-theoretic analysis techniques, enabled by the modeling approach, and 3) new algorithms for adaptive sharing of computational resources between individual features that are guaranteed to satisfy the assumptions made during analysis, realized within a novel mixed-criticality cyber-physical platform architecture.