Demo: An Open-Source and Standards-Based Network-as-a-Service Platform for Cloud VR Gaming
Bibliographic record
Abstract
This paper presents a novel end-to-end quality-on-demand Optical Network-as-a-Service (NaaS) platform that introduces cutting-edge Fifth Generation Fixed Network Advanced (F5G-A) access and transport network capabilities to enterprises, users, and application developers. We demonstrate this platform through a real cloud-based virtual reality (VR) gaming service use-case. The platform is supported by a number of open-source projects (i.e., ETSI OpenSource MANO, ETSI TeraFlowSDN), and its implementation is standards-based (GSMA Open Gateway, Linux Foundation CAMARA, ETSI ISG ZSM and F5G). The platform’s architecture, and its implementation are discussed. The paper also demonstrates a real end-to-end connectivity service via a F5G-A network slice realized by an end-to-end, Layer 3 VPN (L3VPN) service in order to show and validate the proof-of-concept (PoC).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".