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Demo: An Open-Source and Standards-Based Network-as-a-Service Platform for Cloud VR Gaming

2024· article· en· W4400490088 on OpenAlexaff
Hesam Rahimi, Lluís Gifre, Ricard Vilalta, Raül Muñoz, Henry Yu, Yanpeng Wang, Ruilin Cai, Yixiao Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsCloud computingComputer scienceOpen sourceService (business)Open platformWorld Wide WebMultimediaOperating systemSoftware

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.682
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.309
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreMethods

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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