EMSOFT '11 Proceedings of the ninth ACM international conference on Embedded software
On Resource Overbooking in an Unmanned Aerial Vehicle
ICCPS '12 Proceedings of the 2012 IEEE/ACM Third International Conference on Cyber-Physical Systems
Overload provisioning in mixed-criticality cyber-physical systems
ACM Transactions on Embedded Computing Systems (TECS)
Multi-layered scheduling of mixed-criticality cyber-physical systems
Journal of Systems Architecture: the EUROMICRO Journal
Implementation and evaluation of mixed-criticality scheduling approaches for sporadic tasks
ACM Transactions on Embedded Computing Systems (TECS)
Mixed-criticality scheduling on multiprocessors
Real-Time Systems
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Large-scale distributed cyber-physical systems will have many sensors/actuators (each with local micro-controllers), and a distributed communication/computing backbone with multiple 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 continue 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 algorithms used. In this paper, we present a ductility-maximization packing algorithm to complement our previous work on mixed-criticality uniprocessor scheduling [6]. Our packing algorithm, known as Compress-on-Overload Packing (COP) is a criticality-aware greedy bin-packing algorithm that maximizes the tolerance of high-criticality tasks to overloads. We compare the ductility of COP against the Worst-Fit Decreasing (WFD) bin-packing heuristic used traditionally for load balancing in distributed systems, and show that the performance of COP dominates WFD in the average case and can reach close to five times better ductility when resources are limited. Finally, we illustrate the practical use of COP in distributed cyber-physical systems using a radar surveillance application, and provide an overview of the entire process from assigning task criticality levels to evaluating its performance.