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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".