Resource allocation in distributed mixed-criticality cyber-physical systems

Abstract—Large-scale distributed cyber-physical systems will have many sensors/actuators (each with local micro-controllers), and a distributed communication/computing backbone with mul-tiple processors. Many cyber-physical applications will be safety-critical and in many cases unexpected workload spikes are likely to occur due to unpredictable changes in the physical environment. In the face of such overload scenarios, the desirable property in such systems is that the most critical applications con-tinue to meet their deadlines. In this paper, we capture this mixed-criticality property by developing a formal overload-resilience metric called ductility. The generality of ductility enables it to evaluate any scheduling algorithm from the perspective of mixed-criticality cyber-physical systems. In distributed cyber-physical systems, this ductility is the result of both the task-to-processor packing (a.k.a bin packing) and the uniprocessor scheduling al