Evaluating Amazon EC2 Spot Price Prediction Models Using Regression Error Characteristic Curve
Bibliographic record
Abstract
Amazon EC2 offers inactive virtual machines (VM) as spot instances at up to 90% discount. In return, the least expensive option requires the customers' usage to be tolerated with a low availability level agreement. Thus, many studies proposed forecasting and prediction mechanisms to asses in finding the best set of maximum prices. In this paper, we study the model's efficiency in predicting spot EC2 prices with focusing on assessing the performance of forecasting algorithms: RFR, XGBoost, k-NNR, and SVR. Model's evaluation is crucial for measuring the accuracy of predicted prices, thus, we select six metrics for evaluating the forecasting results. We used the top implemented metrics in the related work: MAPE, RMSE, MAE, and MSE. In addition, we assessed the spotted models using the Regression Error Characteristics (REC) curve and the Area under the curve (AUC-REC) in comparison to prior measures. Three aspects are considered while building the models: dataset time per year, training window as 1-day or 1-month ahead and instance location. The trained model applies the cross-validation technique to learn the ideal hyper-parameters that achieve the highest accuracy. However, except for the SVR model, our findings indicate it is unnecessary to use this technique to improve the algorithms' accuracy. Our results investigations display the REC curve and AUC-REC as a superior performance measurements for evaluating models over different accuracy-loss thresholds.
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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 | 0.006 | 0.010 |
| 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 | 0.001 |
| 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".