Dynamic and Parallel Resource Allocation with Analytical Performance Estimation for Virtualized Environment
Bibliographic record
Abstract
In this thesis, we propose an integrated approach for estimating the performance of virtual network resource allocation and a Genetic Algorithm (GA) based mechanism for online dynamic resource allocation in virtualized environment.The integrated approach is empowered by a novel loss network model with Dynamic Routing And Random Topology (DRART), which is combined with some existing models to create a synergy across different levels through an effective recursive process.Numerical results show the proposed integrated approach can provide accurate predictions on the performances of general virtual network embedding algorithms.We propose a virtual link mapping solution, i.e., Segment Based Genetic Algorithm (SBGA), which provides new definitions for genes and chromosomes in the Genetic Algorithm.Our SBGA approach enables parallel processing for searching optimal allocations.Our theoretical analysis shows that the execution time of our approach can be reduced to logarithmic time.To map virtual nodes and links to physical ones in one stage, we further develop an approach, named as GAOne.Our proposed GAOne approach applies the two-color graph coloring in graph theory to guide the crossover process in the Genetic Algorithm (GA) for valid solutions.Our simulation results show that the proposed GAOne approach is fast and efficient for online resource allocation applications in virtualized environment.i At this point, my Ph.D. journey is coming to the end.In the past six years, I've thought of giving up many times.Fortunately, I decided to persevere.First and foremost, I would like to express my deep and sincere gratitude to my supervisor Dr.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".