Predicting task outcome using ensemble machine learning models

One of the most prominent problems cloud service providers face is cloud failure. The main strategy for solving this problem is fault-tolerance management, and a key method for preventing failures is failure prediction. Building a highly accurate predictive model is the primary challenge in failure prediction. In this study, we present machine learning models for predicting both multi-event (five-class) and binary task failures, using Google Cluster Traces from 2011 and 2019. We tested three feature selection methods (SelectKBest, RFE, and Random Forest importance), used PSO-based hyperparameter tuning, and applied a stacking ensemble method. We measured model performance with several metrics. Our results show that the stacking method is consistent across feature sets ( F 1 ≈ 95 % on GCT-2019), but it does not always outperform a single Decision Tree. Testing over eight years shows the model is robust to changes in workload, scheduler policies, and hardware. These results offer a starting point for predicting multi-event task termination in cloud environments.