Automated Design of Network Services from Network Service Requirements
Bibliographic record
Abstract
The ETSI Network Function Virtualization (NFV) has defined the NFV Management and Orchestration (NFV-MANO) framework and a set of concepts for the rapid provisioning, deployment, and management of Network Services (NSs). To do so the NFV-MANO expects as input a description of the NS. Thus, one has to come up with the design of the NS and its descriptor, which contains all the details required for the deployment and management of the NS. In this paper, we propose an approach for automating the design of NSs. The approach starts with NS Requirements (NSReq) - an intent that describes the requested service at a high level of abstraction focusing on its functional, non-functional and/or architectural characteristics. With the help of a Network Function (NF) ontology, which captures the knowledge about NSs, NFs and their relations from past designs and possibly standards, we transform the NSReq through successive steps to a level where Virtual Network Functions (VNFs) can be selected from a VNF catalog and put together in VNF Forwarding Graphs (VNFFGs) to form a NS, which is then dimensioned and tailored according to the non-functional requirements.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".