Context-Aware Graceful Degradation for Mixed-Criticality Scheduling in Autonomous Systems

Autonomous systems are of high complexity and often regarded as mixed-criticality systems (MCSs) in which functions are allocated criticality levels according to risk assessment based on safety standards. Typically, tasks have different real-time requirements across criticality levels, and the estimated worst-case execution times (WCETs) are distinct. Further, limitations in computational resources increase the difficulty of integrating tasks onto one shared hardware platform. Conventionally, all nonsafety critical tasks must be discarded or suspended to guarantee the execution of safety-critical tasks when facing a timing fault. This typically leads to a considerable decrease in the system's Quality-of-Service (QoS). Achieving more graceful degradation is critical to minimizing QoS reduction. This work focuses on tackling timing faults and proposes a novel graceful degradation strategy for use in a mixed-criticality context. Thus, when a system has multiple operational modes depending on the environment or an operational task, our approach can give an effective way of managing degradation to maximize QoS, which is currently not sufficiently recognized in MCS. Furthermore, the proposed causality analysis-based degradation process "bridges the gap" so functional dependencies are considered in scheduling design and thus leads to a graceful degradation that is both feasible and reasonable in functional and nonfunctional terms. The evaluations show that QoS can be better preserved using the proposed context-aware degradation process when compared with more conventional MCS scheduling approaches.