Anomaly Detection-Based Multilevel Ensemble Learning for CPU Prediction in Cloud Data Centers
Bibliographic record
Abstract
In today’s cloud computing era, accurate forecasting of CPU usage is crucial to maximize performance and energy efficiency in data centers. As cloud data centers become more complex and larger in scale, traditional predictive models may require enhancements to incorporate more sophisticated and comprehensive solutions. This paper presents a sophisticated multilevel learning framework specifically tailored to address the requirements of contemporary cloud data centers. The proposed framework synergistically combines anomaly detection and multilevel ensemble learning-based regression prediction to improve CPU usage prediction within cloud data centers. Various anomaly detection techniques are explored in the preliminary data processing stage to identify and address anomalies within the CPU usage trace. Subsequent phases employ multilevel ensemble-based prediction models for accurate data-driven forecasts. By conducting thorough assessments, our model exhibits substantial improvements in both the accuracy of predictions and its resilience to the inherent volatility of cloud environments. Our research provides the foundation for an improved method of predicting CPU utilization, paving the way for advancements in cloud computing resource management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".