Predictive Failure Risk Assessment Using Temporal Dependency Modeling in Distributed Computing Environments
Year: 2026
Type: preprint
Abstract: Predicting adverse operational events in distributed computing environments remains challenging because relevant patterns are often distributed across historical system logs and machine-event records. This study investigates temporal feature-based predictive assessment using three supervised prediction tasks and an additional anomaly-label validation task. The experimental methodology combines chronological event aggregation, historical-window feature construction, block-level sequence representation, and machine-specific event-history analysis. Logistic Regression and Random Forest were evaluated using BGL supercomputer logs, HDFS_v1 block sequences, and Google Cluster machine-event records, while OpenStack logs were examined for injected-anomaly identifier validation. For BGL next-minute alert prediction, Logistic Regression achieved an F1-score of 0.3377 and PR-AUC of 0.2982, outperforming Random Forest, which achieved 0.2887 and 0.1729, respectively. For HDFS prefix-based block-outcome classification, Random Forest achieved an F1-score of 0.4481 and PR-AUC of 0.7325, compared with 0.1983 and 0.6350 for Logistic Regression. For Google subsequent machine-removal prediction, Logistic Regression achieved a higher F1-score of 0.9379, whereas Random Forest obtained a higher PR-AUC of 0.9639 compared with 0.9249. The OpenStack validation identified 120 occurrences of four injected-anomaly VM identifiers in the abnormal log and no occurrences in the two normal logs. These findings demonstrate that the effectiveness of temporal operational features and conventional machine-learning classifiers depends on the prediction target, data characteristics, and evaluation metric. The results provide a dataset-specific experimental baseline for operational event-risk assessment rather than direct evidence of independently verified physical failure prediction.
Source: Research Square
Author: Umma Habiba Sultana
Institution: Trine University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225789875