Collaborative and Verifiable VNF Management for Metaverse With Efficient Modular Designs
Bibliographic record
Abstract
The metaverse is envisioned to create immersive and virtual worlds for people to experience interoperable 3D applications. However, the real-time, interactive, and multimedia characteristics of the metaverse applications require strict quality-of-service (QoS) on the underlying networking architecture, including high throughput, ultra-low delay, and human-centric service configurations. Network function virtualization (NFV)-enabled networking resource management can provide a promising solution to service-oriented QoS satisfaction for metaverse users. In this paper, we propose a blockchain-based collaborative and verifiable virtualized network function (VNF) management scheme for metaverse, named BVNF+. BVNF+ enables multiple network providers across different trust domains to abstract their services as VNFs and collaboratively manage end-to-end network slices for human-centric network services in metaverse. To address the design challenge of balancing the on-chain and off-chain overheads, we decouple the computations of VNF queries into modular components based on software and hardware verifiable computation (vc) approaches. Our modular strategy can achieve on/off-chain computation and communication efficiency while keeping low usage of the secure hardware. We conduct security analysis and extensive experiments based on a real-world blockchain testing network. The analysis and experimental results demonstrate that BVNF+ is both secure and efficient as compared with the existing works.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".