Revisiting Dynamic Scheduling of Control Tasks: A Performance-Aware Fine-Grained Approach

Abstract

Modern cyber-physical systems (CPSs) employ an increasingly large number of software control loops to enhance their autonomous capabilities. Such large task sets and their dependencies may lead to deadline misses caused by platform-level timing uncertainties, resource contention, etc. To ensure the schedulability of the task set in the embedded platform in the presence of these uncertainties, there exist co-design techniques that assign task periodicities such that control costs are minimized. Another line of work exists that addresses the same platform schedulability issue by skipping a bounded number of control executions within a fixed number of control instances. Considering that control tasks are designed to perform robustly against delayed actuation (due to deadline misses, network packet drops etc.) a bounded number of control skips can be applied while ensuring certain performance margin. Our work combines these two control scheduling co-design disciplines and develops a strategy to adaptively employ control skips or update periodicities of the control tasks depending on their current performance requirements. For this we leverage a novel theory of automata-based control skip sequence generation while ensuring periodicity, safety and stability constraints. We demonstrate the effectiveness of this dynamic resource sharing approach in an automotive Hardware-in-loop setup with realistic control task set implementations.