<scp>PCNN</scp>‐<scp>BCMO</scp>: A Novel Deep Learning Espoused Task Scheduling Approach for Enhancing Energy Efficiency of Cloud Data Centers
Bibliographic record
Abstract
ABSTRACT The rapid development of cloud computing and data centers has intensified the need for efficient task scheduling to enhance energy efficiency and resource utilization. This study presents a novel task scheduling approach leveraging a part‐based convolutional neural network (PCNN) optimized with balancing composite motion optimization (BCMO) to address these challenges. Historical data from cloud data centers is pre‐processed utilizing guided box filtering (GBF) to eliminate noise, allowing the PCNN to accurately predict and schedule tasks. The BCMO optimization method fine‐tunes the PCNN's weight parameters, ensuring effective scheduling while minimizing power consumption. Implemented in JAVA, the proposed method achieves remarkable accuracies of 99.67% and 99.78% on NASA and Saskatchewan HTTP traces benchmark datasets, respectively, outperforming existing models.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".