<scp>COSCO2</scp>: <scp>AI</scp>‐augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers
Bibliographic record
Abstract
Abstract Workload prediction is the necessary factor in the cloud data center for maintaining the elasticity and scalability of resources. However, the accuracy of workload prediction is very low, because of redundancy, noise, and low accuracy for workload prediction in cloud data center. Therefore, in this article, a tree hierarchical deep convolutional neural network (T‐CNN) optimized with sheep flock optimization algorithm based work load prediction is proposed for sustainable cloud data centers. Initially, the historical data from the cloud data center is preprocessed using kernel correlation method. The proposed T‐CNN approach is used for workload prediction in dynamic cloud environment. The weight parameters of the T‐CNN model are optimized by sheep flock optimization algorithm. The proposed COSCO2 method has accurately predicts the upcoming workload and reduces extravagant power consumption at cloud data centers. The proposed approach is evaluated utilizing two benchmark datasets: (i) NASA, (ii) Saskatchewan HTTP traces. The simulation of this model is implemented in java tool and the parameters are calculated. From the simulation, the proposed method attains 20.64%, 32.95%, 12.05%, 32.65%, 26.54% high accuracy, and 27.4%, 26%, 23.7%, 34.7%, 36.5% lower energy consumption for validating NASA dataset, similarly 20.75%, 19.06%, 29.09%, 23.8%, 20.5% high accuracy, 20.84%, 18.03%, 28.64%, 30.72%, 33.74% lower energy consumption for validating Saskatchewan HTTP traces dataset than the existing approaches, like auto adaptive differential evolution algorithm BiPhase adaptive learning‐based neural network, error preventive score in time series forecasting models, time series forecasting methods for cloud data workload prediction, and self‐directed workload forecasting method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".