LAVP: A Latency-Aware Virtual Network Function Placement Strategy for Service Function Chain in Network Function Virtualization
Bibliographic record
Abstract
Nowadays, businesses have benefited greatly from Network Function Virtualization (NFV), while users have gained newexperiences. By transforming network functions intoVirtual Network Functions (VNFs) running on High-Volume Servers (HVSs) instead of using dedicated hardware devices, NFV enables the reduction of Capital Expenditure (CAPEX) and Operation Expenditure (OPEX). However, NFV is now facing numerous issues, including performance, stability, and security. In this article, we examine the influence of VNF placement on the end-to-end latency. To reduce latency, the Latency-Aware VNF Placement (LAVP) algorithm, a heuristic technique, is offered to deal with the VNF placement problem by finding the best path for the Service Function Chain (SFC) while ensuring VNF ordering. The VNFs can then be resourced and deployed on the nodes along the path. Our algorithm is implemented on two different topologies and compared with other algorithms, namely First Fit, Best Fit,Worst Fit, and AvReStrategy. The experimental results show that our proposed solution can significantly reduce delays for the service chain with less execution time compared to other strategies.
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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 | 0.001 |
| 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".