Enabling Quality-on-Demand and Service Differentiation on a Novel Network-as-a-Service Platform Using Slicing Technology for Control and Management of Optical Networks
Bibliographic record
Abstract
This paper presents a comprehensive Network-as-a-Service (NaaS) platform designed to enhance the deployment and management of all-optical access and transport networks. We demonstrate our platform with a use-case of cloud-based AR/VR gaming service that requires high bandwidth and ultra-low latency, through the slicing of Passive Optical Network (PON) and optical transport network. Building on our previous work that detailed the overall architecture of our solution and the fine-grain Optical Transport Network (fgOTN) technology, this paper introduces the novel aspect of automated PON slicing, achieving a fully end-to-end solution. Leveraging ETSI F5G and ETSI ZSM standards and integrating with ETSI TeraFlowSDN and ETSI Open-Source MANO (OSM) for orchestration and management, our platform offers a seamless and immersive experience for latency-sensitive applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".