Introducing Virtual Security Functions into Latency-aware Placement for\n NFV Applications
Bibliographic record
Abstract
The shift towards a completely virtualized networking environment is\ntriggered by the emergence of software defined networking and network function\nvirtualization (NFV). Network service providers have unlocked immense\ncapabilities by these technologies, which have enabled them to dynamically\nadapt to user needs by deploying their network services in real-time through\ngenerating Service Function Chain (SFCs). However, NFV still faces challenges\nthat hinder its full potentials, including availability guarantees, network\nsecurity, and other performance requirements. For this reason, the deployment\nof NFV applications remains critical as it should meet different service level\nagreements while insuring the security of the virtualized functions. In this\npaper, we tackle the challenge of securing these SFCs by introducing virtual\nsecurity functions (VSFs) into the latencyaware deployment of NFV applications.\nThis work insures the optimal placement of the SFC components including the\nsecurity functions while considering the performance constraints and the VSFs'\noperational rules such as, functions' alliance, proximity, and anti-affinity.\nThis paper develops a mixed integer linear programming model to optimally place\nall the requested SFCs while satisfying the above constraints and minimizing\nthe latency of every SFC and the intercommunication delay between the SFC\ncomponents. The simulations are evaluated against a greedy algorithm on the\nvirtualized Evolved Packet Core use case and have shown promising results in\nmaintaining the security rules while achieving minimum delays.\n
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".