Abstract

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. 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

Highlights

  • Industrial-scale cyber-physical systems consist of multiple sensor, actuation, and computation subsystems running on a distributed platform

  • Our three Compress-on-Overload Packing (COP) algorithm variants are compared to Worst-Fit Decreasing (WFD) given that it is designed to balance the load, and the slack, across all the available processors

  • To maximize the ductility on distributed systems, we developed a new bin-packing algorithm for mixed-criticality real-time systems known as Compress-on-Overload Packing (COP) that works on top of the zero-slack rate-monotonic scheduler

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Summary

INTRODUCTION

Industrial-scale cyber-physical systems consist of multiple sensor, actuation, and computation subsystems running on a distributed platform. The physical phenomena interacting with the system impose strict timing constraints and potentially variable workloads on different pipeline stages In such systems, the sensing and actuation tasks are typically pre-allocated to their individual dedicated subsystems, while the scheduling of process tasks and their allocation to processors presents an important resource allocation problem. We present our bin-packing algorithm for mixed-criticality systems known as Compresson-Overload Packing (COP) that extends the asymmetric protection scheme from ZSRM for distributed systems. We evaluate this algorithm using the ductility metric, and compare its performance against the WFD algorithm traditionally used for load balancing. To the best of our knowledge, this is the first work to study the mixed-criticality scheduling problem in the context of distributed systems

Related Work
RESOURCE ALLOCATION IN MIXED-CRITICALITY SYSTEMS
ZSRM Overview
Illustration
Ductility Matrix
Normalized Ductility
EVALUATION
A RADAR SURVEILLANCE CASE STUDY
Task Allocation and Scheduling
CONCLUSIONS
Full Text
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