Checkpointing-Aware End-to-End Data Age Analysis of Task Chains under Transient Faults

Safety-critical real-time systems are conventionally designed to meet specific hard real-time deadline requirements. Many applications in such systems consist of task chains, which exhibit complex temporal dependencies based on the activation rates of the tasks. As a result, there has been an increasing interest in analyzing the age of data (i.e., the time elapsed since the data was first updated) that propagates through the task chains as an important system performance parameter. Transient faults in safety-critical systems can lead to undesirable consequences. One of the most common methods used for fault tolerance in such systems is checkpointing. However, the interference due to checkpointing can significantly impact the data age, reducing the predictability of the system's performance.This paper proposes an analytical framework for calculating the data age in real-time task chains with checkpointing for transient faults in both single-core and multi-core platforms. The proposed analysis has been validated by comparing it with extensive simulations of the execution of real-time task chain-based systems under faults. It shows comparable data age values with low time complexity.