Predictive Modeling of Resource Utilization in Cloud Data Centers Using Multi-Output Regression
Bibliographic record
Abstract
Integrating accurate resource usage prediction with cloud management systems is critical to optimize resource utilization and improve operational efficiency. The dynamic and non-linear usage patterns of resources in cloud data centers pose significant challenges for predictive modeling. Traditional single-output models, designed to predict a single value, often struggle to capture the complexities of series resource usage patterns. Current predictive models do not consider the interdependencies and interactions of various resource usage patterns in a sequence. Therefore, it is necessary to develop more robust predictive methods that can predict a series of resource usage patterns. This study introduces an innovative predictive model that uses multi-output regression combined with time series windowing, usage pattern clustering, and various transformation methods to predict a series of resource usage with high precision for heterogeneous cloud computing systems. Transformation methods include a power transformer to normalize the data distribution, a standard scaler to standardize the feature space, a polynomial transformation to enhance model complexity, and principal component analysis to reduce the dimensionality of the training feature space. The proposed model is evaluated using multi-output regression benchmarks with real cloud workloads and various evaluation metrics. The results demonstrated that the proposed approach significantly improves prediction accuracy while reducing training costs, offering substantial potential for improved performance and efficiency in cloud computing operations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".