Predictive Failure Risk Assessment Using Temporal Dependency Modeling in Distributed Computing Environments

Technical Summary: Predictive Failure Risk Assessment Using Temporal Dependency Modeling in Distributed Computing Environments

1. Problem Statement

The central research problem addresses the limited predictive interpretation of historical operational events in distributed computing environments when temporal dependencies are insufficiently considered. While system logs contain continuous records of events, warnings, and status changes, isolated anomalies do not necessarily indicate an approaching failure. Relevant predictive information is often distributed across multiple events, relying on their order, frequency, and temporal relationships.

Furthermore, a significant challenge exists in the heterogeneity of publicly available datasets. Different datasets (e.g., BGL, HDFS, Google Cluster) utilize distinct annotation structures describing different phenomena (alerts, block outcomes, machine removals). These labels cannot be automatically interpreted as equivalent physical failure outcomes. The research aims to distinguish historical patterns associated with subsequent adverse events from isolated abnormalities or routine activity, while preserving the specific meaning of each dataset's original labels rather than forcing them into a unified "failure" category.

2. Methodology

The study employs a comparative experimental framework using four publicly available distributed-computing datasets: BGL (supercomputer logs), HDFS_v1 (distributed file system block sequences), OpenStack (infrastructure logs), and Google Cluster (machine-event records).

Data Preprocessing and Feature Construction:

• BGL: Logs were aggregated into minute-level intervals. Historical observation windows consisted of the ten preceding minutes, capturing event counts and alert frequencies. The target was the presence of an alert-category message in the immediately following minute.
• HDFS_v1: Block sequences were analyzed using a prefix-based approach. The initial half of each event sequence was converted into 29 event-template frequency features. The target was the recorded block outcome (Normal vs. Anomalous).
• Google Cluster: Records were grouped by machine identifier and sorted by timestamp. Features included current/preceding event codes, elapsed time, and resource capacity (CPU/memory). The target was the event category of the next recorded event (specifically, machine removal).
• OpenStack: Used for validation rather than predictive modeling. The study verified the presence of four injected-anomaly VM identifiers in abnormal logs versus normal logs.

Predictive Models:
Two supervised classifiers were evaluated across the datasets:

1. Logistic Regression (LR): Implemented with standardization, class weighting, and regularization.
2. Random Forest (RF): Implemented as an ensemble of decision trees with class-balanced sampling.

Evaluation Strategy:
The study utilized dataset-specific partitioning strategies to prevent information leakage:

• BGL: Chronological split (70% train, 15% validation, 15% test).
• HDFS: Stratified block-level split.
• Google: Machine-grouped split (75% train, 25% test).
• Metrics: Performance was assessed using Precision, Recall, F1-score, Precision-Recall Area Under the Curve (PR-AUC), ROC-AUC, and Brier score.

3. Key Results

The experimental results demonstrate that model performance is highly dependent on the specific prediction target and dataset characteristics. No single classifier outperformed the other across all tasks.

• BGL (Next-Minute Alert Prediction):

  • Logistic Regression outperformed Random Forest, achieving an F1-score of 0.3377 (vs. 0.2887 for RF) and a PR-AUC of 0.2982 (vs. 0.1729).
  • Random Forest achieved a lower Brier score (0.0591 vs. 0.1310), indicating lower average squared probability error, but failed to match LR's classification performance at the selected threshold.
  • Both models struggled with the high class imbalance (only ~7.34% alerts), resulting in low recall.

• HDFS_v1 (Prefix-Based Block-Outcome Classification):

  • Random Forest significantly outperformed Logistic Regression, achieving an F1-score of 0.4481 (vs. 0.1983 for LR) and a PR-AUC of 0.7325 (vs. 0.6350).
  • RF demonstrated superior recall (0.9014) and identified a substantially larger proportion of anomalous blocks compared to LR.

• Google Cluster (Subsequent Machine-Removal Prediction):

  • Logistic Regression achieved a slightly higher F1-score (0.9379) compared to Random Forest (0.9297).
  • However, Random Forest achieved higher PR-AUC (0.9639 vs. 0.9249) and ROC-AUC, along with a marginally lower Brier score, suggesting better ranking discrimination and probability calibration despite the lower F1-score.

• OpenStack Validation:

  • The study confirmed that all four injected-anomaly VM identifiers appeared 30 times each (120 total) in the abnormal log and zero times in the two normal logs, validating the consistency of the anomaly labels.

4. Key Contributions

1. Task-Specific Evaluation: The study distinguishes between different types of operational outcomes (alert forecasting, block-level anomaly classification, and machine-event prediction) rather than treating all anomalies as equivalent physical failures. This provides a more precise basis for evaluating predictive models under varying annotation structures.
2. Comparative Baseline Analysis: The research provides a dataset-specific experimental baseline comparing conventional statistical (Logistic Regression) and nonlinear (Random Forest) approaches. It highlights that the "best" model depends on the target definition and the chosen evaluation metric (e.g., LR favored for F1 in BGL/Google, RF favored for F1 in HDFS).
3. Reproducible Feature Engineering: The paper details a reproducible procedure for transforming raw operational records into historical feature representations, including timestamp processing, sequence construction, and dataset-specific prediction targets.
4. Interpretation of Limitations: The study explicitly clarifies the distinction between predicting dataset-defined labels (alerts, block outcomes, removals) and verifying physical hardware failures, preventing over-interpretation of the results.

5. Significance and Claims

The paper claims to provide a dataset-specific experimental baseline for operational event-risk assessment. It does not claim to have solved the problem of physical failure prediction or to have established a universal classifier for all distributed systems.

The primary significance lies in demonstrating that:

• The effectiveness of temporal operational features and conventional machine-learning classifiers is contingent upon the prediction target, data characteristics, and the specific evaluation metric used.
• High F1-scores do not guarantee high probability discrimination (PR-AUC), and vice versa, necessitating multi-metric evaluation.
• Historical event characteristics contain predictive information for dataset-defined adverse events, but these results should be interpreted as operational event risks (e.g., the likelihood of an alert or a machine removal) rather than confirmed physical failure probabilities.

The author concludes that future work requires independently verified failure labels, longer prediction horizons, and more complex temporal models (e.g., LSTM, GRU) to move beyond these established baselines.