Time Series Forecasting using Facebook Prophet for Cloud Resource Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The heterogeneous nature of workloads running in cloud environments makes future resource usage prediction a complicated problem. Virtual machines can be described in five types of resource utilization patterns: steady, trending, seasonal, cyclic, and bursty behavior. Understanding these usage patterns and behaviors can enhance resource management on cloud data centers, especially VM scheduling, power management, and server health management systems. This paper applies the Facebook Prophet forecast framework on Microsoft Azure VM workload to predict future resource utilization required by the running tasks. We conclude that utilizing data preprocessing and transformation on real virtual machine traces, and incorporating an automatic model hyperparameter tuning process, can significantly increase forecasting accuracy with an average percentage change of over 85%. Furthermore, cloud providers can learn from their data center workloads and employ various forecasting models to gain substantial improvements in cost-efficient resource management.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it