Abstract:  The complexity of distributed systems often leads to node failures and other fault issues， which can reduce the system’s service capability and quality. To improve the reliability and stability of distributed systems， this paper proposes a fault prediction method for distributed systems based on XGBoost and LightGBM. Firstly， a multidimensional data preprocessing method is adopted to clarify data features， facilitating classification by the prediction model. Then， the extreme gradient boosting algorithm （XGBoost） is used to train the processed dataset， and feature selection is performed based on feature importance to enhance the model’s generalization ability and reduce overfitting. Finally， the optimized LightGBM algorithm is used for model training on the dataset. Experimental results show that the proposed method outperforms other classification models in terms of accuracy， precision， and recall. Compared to RF， XGBoost， and LightGBM models， the proposed method improves accuracy by 4.89%， 3.52%， and 1.39%， and enhances the F1 score by 5.56%， 3.91%， and 1.53%， respectively. This validates that the proposed model can be efficiently applied to distributed system fault type prediction scenarios.

Key words: distributed systems, fault detection, machine learning, XGBoost, LightGBM