Application of an Enhanced Self-adapting Differential Evolution Algorithm to Workload Prediction in Cloud Computing
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Bibliographic record
Abstract
The demand for workload prediction approaches has recently increased to manage the cloud resources, improve the performance of the cloud services and reduce the power consumption. The prediction accuracy of these approaches affects the cloud performance. In this application paper, we apply an enhanced variant of the differential evolution (DE) algorithm named MSaDE as a learning algorithm to the artificial neural network (ANN) model of the cloud workload prediction. The ANN prediction model based on MSaDE algorithm is evaluated over two benchmark datasets for the workload traces of NASA server and Saskatchewan server at different look-ahead times. To show the improvement in accuracy of training the ANN prediction model using MSaDE algorithm, training is performed with other two algorithms: the back propagation (BP) algorithm and the self-adaptive differential evolution (SaDE) algorithm. Comparisons are made in terms of the root mean squared error (RMSE) and the average root mean squared error (ARMSE) through all prediction intervals. The results show that the ANN prediction model based on the MSaDE algorithm predicts the cloud workloads with higher prediction accuracy than the other algorithms compared with.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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