VAPNIC: A VersAtile shortest path-free VNF Placement using a divide-and-coNquer tactIC
Bibliographic record
Abstract
Orchestration mechanisms play a pivotal role in assisting service providers in deploying their increasingly complex virtual network services seamlessly thanks to Network Function Virtualization (NFV) and Software-defined networking (SDN) technology enablers. Unfortunately, existing state-of-the-art orchestration techniques suffer from non-scalability and time-efficiency aptitude when the services require VNFs to be distributed across cloud and edge environments with complex dimensions. Furthermore, they provide competitive solutions in good execution time only for small-scale scenarios (e.g., in seconds for 50 nodes) but generally require an exorbitant amount of time to converge towards feasible solutions for medium-scale or even large-scale schemes. This paper proposes VAPNIC: an innovative approach that solves the VNF placement and chaining problem with lower algorithmic complexity using a disjoint-set data structure aided divide-and-conquer strategy. Our method's unique design is the non-use of any existing shortest path search algorithms to chain the virtual network functions. To the best of our knowledge, this is the first work that strives to tackle the placement and chaining of the VNFs from a distinctive perspective in the case of medium-and-large scale scenarios with a fast and scalable heuristic that exploits the divide-and-conquer design paradigm based on multi-branched recursion. Experimental results indicate that VAPNIC outperforms existing approaches in acceptance rate, resource utilization, scalability, and time efficiency.
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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 | 0.001 | 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) | 0.003 | 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".