VNF Placement Problem: A Multi-Tenant Intent-Based Networking Approach
Bibliographic record
Abstract
Network Function Virtualization (NFV) has revolutionized the way networking services are offered and deployed. Moving away from a rigid and hardware-centric approach, where expensive and dedicated network components are used, NFV is now leveraging standard x86 servers, where softwarized images of network functions (NFs) can be hosted as Virtual Machines (VNFs) or containers (CNFs). However, in terms of deploying, configuring, and interconnecting these softwarized images, a lot of manual intervention is required. To this end, the Intent-Based Networking (IBN) paradigm has emerged, which has as a goal to automate the network configuration by translating a high-level and abstract request of a network service into a detailed policy description. Usually, IBN and NFV are studied separately, even though in reality they are highly correlated and can benefit from each other. In particular, network services can be expressed as abstract service requirements from the users, where through an IBN System (IBNS) will be translated into specific network policies and a VNF/CNF deployment solution, called VNF Placement solution. Accordingly, in this paper, we aim to combine these two technologies together in order to automate the deployment of the VNFs in a Cloud-based infrastructure, while supporting multitenancy and intent refinement. Our results reveal that an IBN-based VNF placement solution can successfully offer network services, expressed as user intents, in such a way that the network services are automatically configured according to the quality of service and security requirements included in the intent.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".