Leveraging Weakly-hard Constraints for Improving System Fault Tolerance with Functional and Timing Guarantees

• Published in: Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design pp. 1 - 9
• Main Authors: Liang, Hengyi, Wang, Zhilu, Jiao, Ruochen, Zhu, Qi
• Format: Conference Proceeding
• Language: English
• Published: Association on Computer Machinery 02.11.2020
• Subjects: Analytical models; approximate system control cost; deadline misses; EED; embedded systems; EOC; fault tolerance; fault tolerance techniques; Fault tolerant systems; functional correctness; intermittent faults; meta-heuristic algorithm; real-time systems; safety-critical real-time systems; scheduling; simulated annealing; simulated annealing procedure; Stability analysis; stringent constraints; system control cost; system fault tolerance; Task analysis; task deadline; task execution pattern; Timing; timing guarantees; Transient analysis; transient faults; TWCA; typical worst-case analysis; weak-hard constraints; weakly-hard; weakly-hard constraints
• ISSN: 1558-2434
• DOI: 10.1145/3400302.3415717

Many safety-critical real-time systems operate under harsh environment and are subject to soft errors caused by transient or intermittent faults. It is critical and yet often very challenging to apply fault tolerance techniques in these systems, due to resource limitations and stringent constraints on timing and functionality. In this work, we leverage the concept of weakly-hard constraints, which allows task deadline misses in a bounded manner, to improve system's capability to accommodate fault tolerance techniques while ensuring timing and functional correctness. In particular, we a) quantitatively measure control cost under different deadline hit/miss scenarios and identify weak-hard constraints that guarantee control stability; b) employ typical worst-case analysis (TWCA) to bound the number of deadline misses and approximate system control cost; c) develop an event-based simulation method to check the task execution pattern and evaluate system control cost for any given solution; and d) develop a meta-heuristic algorithm that consists of heuristic methods and a simulated annealing procedure to explore the design space. Our experiments on an industrial case study and synthetic examples demonstrate the effectiveness of our approach.