Earticle

Cloud computing has become a critical infrastructure for large-scale data processing and machine learning applications. However, task failures frequently occur due to distributed resources and dynamic operating environments, leading to service delays and resource waste. To address this issue, this paper proposes a task failure prediction system based on stacking ensemble learning. The proposed system is structured in three layers: log collection, prediction, and result application. Base model predictions are integrated by a metamodel to produce the final outcome. Experiments conducted with the Google Cluster Trace 2019 dataset demonstrate that the proposed system outperforms single models in terms of prediction accuracy and stability, providing a robust and scalable framework suitable for real-world cloud environments.