A Probabilistic Approach for Local Diagnosis in Large Multiprocessor Systems
Qifan Zhang, Shuming Zhou, Sun‐Yuan Hsieh · IEEE Transactions on Networking · 2025
Fault diagnosis is constantly crucial to maintain a high level of multiprocessor systems’ reliability. In multiprocessor systems, global fault diagnosis has been extensively investigated under both deterministic and probabilistic models, while local fault diagnosis has only been committed to the deterministic models, such as PMC model and MM$^*$model. This work focuses on a probabilistic approach for local diagnosis at a node within the mixed structure under the PMC diagnostic model so that the state of this node can be identified correctly by utilizing maximum a posteriori probability. This work is devoted to the quantitative metric on global reliability of multiprocessor systems in terms of local fault probability of node under microscale. The proposed strategy effectively reduces diagnostic delays and enhances system response in practical applications and thus improves the efficiency and accuracy of fault detection. In addition, the probabilistic approach reduces the effect of uncertainty on the fault diagnosis, which in turn improves the reliability and safety of the system. In this work, we first perform a more precise syndrome analysis for this mixed structure under the PMC model by virtue of local testing results, and suggest a modified local diagnosis algorithm calledMLDA. Subsequently, we implement the maximum a posteriori probabilistic local diagnosis algorithm calledMAPPLDAfor the mixed structure under the probabilistic PMC diagnostic model. Finally, numerical simulation results confirm the effectiveness of the syndrome analysis approach and the maximum a posteriori probability approach for the mixed structure when the node failure probability is very small.