A hybrid NFV/In-Network Computing MANO Architecture for provisioning Holographic Applications in the Metaverse
Bibliographic record
Abstract
Innovative holographic applications such as holographic concerts have recently emerged. They are expected to play an important role in the Metaverse. Hybrid Network Function Virtualization (NFV) / IN-Network Computing (INC) network infrastructures are needed to provision them as recently shown in the literature. INC is an emerging technology that aims to distribute the computational workload across the network by placing computational tasks on programmable devices (e.g., routers or switches). However, the integration of INC in existing infrastructures does face significant management and orchestration challenges. Although the ETSI Management and Orchestration (MANO) architectural framework designed for 5G facilitates application provisioning in networks that are NFV enabled, it lacks support for networks that are INC enabled. Therefore it is necessary to have a new MANO architecture in order to provision applications which have both NFV and INC components. This paper proposes a hybrid NFV-INC MANO architecture for provisioning holographic applications in hybrid NFV/INC environment. The proposed architecture is an extension of the ETSI NFV MANO. It will certainlyplay an important role in 6G since many applications foreseen for 6G will have the same stringent requirements as holographic applications. It is evaluated through a proof of concept prototype. The following tools were used for the prototype: Open Source MANO (OSM) and Mininet emulator.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".