Principal component analysis-enhanced ensemble learning models for proactive failure prediction in cloud-based systems

Abstract

Cloud computing environments require high availability and scalability, making proactive failure management essential for ensuring system reliability, security, and consistent performance. Effective failure prediction significantly reduces downtime, improves disaster recovery processes, and maintains uninterrupted service delivery. This paper presents an optimized machine learning framework for predicting failures in cloud infrastructures by integrating principal component analysis (PCA) with advanced ensemble learning models. The study employs three prominent models-random forest (RF), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM)-enhanced through PCA to improve feature representation and overall predictive accuracy. Key operational metrics, including class scheduling, memory usage, central processing unit utilization, event instances, and task priority, are used as features. The Google 2019 cluster dataset is utilized, and preprocessing steps involve handling missing data, scaling numerical attributes, and encoding categorical variables to ensure data quality. Experimental results reveal that PCA-enhanced RF, CatBoost, and LightGBM achieve superior accuracies of 94.31%, 97.17%, and 98.36%, respectively, outperforming their standard counterparts. These outcomes highlight the effectiveness of PCA-integrated ensemble learning and underscore its potential for real-time cloud failure prediction and automated fault monitoring in large-scale distributed environments.

Keywords

Cloud-based systems / Failure prediction / Random forest / CatBoost / Light gradient boosting machine / Principal component analysis / Likelihood of failure

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