Dynamic Task Replication With Imperfect Fault Detection in Multicore Cyber-Physical Systems

Abstract

Task replication is a common technique for achieving fault tolerance. However, its effectiveness is limited by the accuracy of the fault detection mechanism; imperfect detection imposes a ceiling on achievable reliability. While perfect fault detection mechanisms offer higher reliability, they introduce significant overhead. To address this, we introduce Dynamic Task Replication, a fault tolerance technique that dynamically determines the number of replicas at runtime to overcome the limitations of imperfect fault detection. Our primary contribution, Reliability-Aware Replica-Efficient Dynamic Task Replication, optimizes this approach by minimizing the expected number of replicas while achieving the desired reliability target. We incorporate actual execution times into the reliability assessment. Additionally, we propose the Energy-Aware Reliability-Guaranteeing scheduling technique, which integrates our optimized replication method into hard real-time systems and leverages Dynamic Voltage and Frequency Scaling to minimize energy consumption while ensuring reliability and system schedulability. Experimental results demonstrate that our method requires 24% fewer replicas on average than the N-Modular Redundancy technique, with the advantage increasing to 58% for tasks with low base reliabilities. Furthermore, our scheduling technique significantly conserves energy and enhances feasibility compared to existing methods across diverse system workloads.