ESP-VDCE: Energy, SLA, and Price-driven Virtual Data Center Embedding
Bibliographic record
Abstract
In this work, we present a multi-objective Virtual Data Center Embedding (VDCE) scheme for multi-domain cloud computing setups. The primary focus of the proposed scheme is on -Energy minimization, SLA assurance, and reduced energy Prices; and is named as ESP-driven VDCE. In the preliminary phase of this work, we formulate the proposed scheme as an optimization problem. However, due to the intractability of the formulated problem, its is remodelled and divided into three sub-problems (SP), i.e., data center identification, virtual machine mapping, and virtual link embedding. The output of one SP serves as an input to the next SP, such that the search space can be significantly narrowed. Finally, the proposed approach for VDCE is extensively validated against other algorithms. The obtained results indicate that the proposed ESP-driven VDCE approach achieves almost 7.6% more energy-aware embeddings with 11.5% higher SLA levels and approximately 23% lower energy expenses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".