Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding
Bibliographic record
Abstract
Assigning virtual network resources to physical network components, called Virtual Network Embedding, is a majorchallenge in cloud computing platforms. In this paper, we propose a memetic elitist pareto evolutionary algorithmfor virtual network embedding problem, which is called MEPE-VNE. MEPE-VNE applies a non-dominated sortingbasedmulti-objective evolutionary algorithm, called NSGA-II, to reduce computational complexity of constructinga hierarchy of non-dominated Pareto fronts and assign a rank value to each virtual network embedding solutionbased on its dominance level and crowding distance value. Local search is applied to enhance virtual networkembedding solutions and speed up convergence of the proposed algorithm. To reduce loss of good solutions, MEPEVNEensures elitism by passing virtual network embedding solutions with best fitness values to next generation.Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensivesimulations, which show that the proposed algorithm improves virtual network embedding by increasing acceptanceratio and revenue while decreasing the cost incurred by substrate network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".