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Field-programmable gate arrays (FPGAs) offer the flexibility of general-purpose processors along with the performance efficiency of dedicated hardware that essentially renders it as a platform of choice for modern-day robotic systems for achieving real-time performance. Such robotic systems when deployed in harsh environments often get plagued by faults due to extreme conditions. Consequently, the real-time applications running on FPGA become susceptible to errors which call for a reliability-aware task scheduling approach, the focus of this article. We attempt to address this challenge using a hybrid offline-online approach. Given a set of periodic real-time tasks that require to be executed, the offline component generates a feasible preemptive schedule with specific preemption points. At runtime, these preemption events are utilized for fault detection. Upon detecting any faulty execution at such distinct points, the reliability-aware scheduling approach, RASA, orchestrates the recovery mechanism to remediate the scenario without jeopardizing the predefined schedule. Effectiveness of the proposed strategy has been verified through simulation-based experiments and we observed that the RASA is able to achieve 72% of task acceptance rate even under 70% of system workloads with high fault occurrence rates.

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Reproducing a failure is the first and most important step in debugging because it enables us to understand the failure and track down its source. However, many programs are susceptible to nondeterministic failures that are hard to reproduce, which makes debugging extremely difficult. We first address the reproducibility problem by proposing an OS-level replay system for a uniprocessor environment that can capture and replay nondeterministic events needed to reproduce a failure in Linux interactive and event-based programs. We then present an analysis method, called replay analysis, based on the proposed record and replay system to diagnose concurrency bugs in such programs. The replay analysis method uses a combination of static analysis, dynamic tracing during replay, and delta debugging to identify failure-inducing memory access patterns that lead to concurrency failure. The experimental results show that the presented record and replay system has low-recording overhead and hence can be safely used in production systems to catch rarely occurring bugs. We also present few concurrency bug case studies from real-world applications to prove the effectiveness of the proposed bug diagnosis framework.