Research on Distributed System Fault Diagnosis Based on Logistic Regression
Fangke Yan, Shuangbing Wen, Cheng-wei Liao, Jun Li, Tao “Eric” Hu · 2024
With the advent of the big data era, distributed systems have become the mainstream as the main solution for information storage and processing. However, compared with traditional systems, the scale of distributed systems is larger, and the structure is more complex, which leads to a higher frequency of failures and significantly increased difficulty and complexity of operation and maintenance. Therefore, aiming at the problem of fault diagnosis, this paper uses machine learning algorithms to preprocess the KPI index data and extract features when the distributed system fails, uses the Logistic Regression algorithm to diagnose the fault, and compares with Linear Support Vector Classifier, K-Nearest neighbor Classifier, AdaBoost Classifier, RandomForest Classifier and Decision Tree algorithm. The experimental results show that the logistic regression algorithm for fault diagnosis is higher than the other models.