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Record W4239783681 · doi:10.1109/cnsm.2016.7818439

Enhanced real time content delivery using vCPE and NFV service chaining

2016· article· en· W4239783681 on OpenAlexaff
Pouya Yasrebi, Hadi Bannazadeh, Alberto Leon‐Garcia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChainingComputer scienceBandwidth (computing)Service (business)Computer networkNetwork Functions VirtualizationDistributed computingQuality of serviceOperating systemCloud computing

Abstract

fetched live from OpenAlex

Real-time content delivery (RTCD) systems have become a prominent aspect of telecommunications as evidence by popularity of news-casting, real-time event subscription / publication and live media streaming. Unlike conventional content delivery systems, RTCDs need to deliver processed information to users in real time. This may require the network to handle some of the processing closer to the users to efficiently use the bandwidth consumed by the applications. The combination of Network Function Virtualization (NFV) and service chaining is a promising solution to address this challenge. Our work applies a service chaining algorithm to place NFV modules of an RTCD application in a Software Defined Infrastructure (SDI), where virtualized Customer Premise Edges (vCPEs), possessing scarce resources, are employed. We suggest containers to efficiently pack VNFs into vCPEs. Our objective is to maximize the total number of chains that can be serviced in the RTCD application. To optimally chain the NFV modules, a heuristic algorithm is proposed and evaluated. Using simulations, we show that our algorithm with the help of vCPEs can support higher number of users while providing high-level service quality.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.229
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations1
Published2016
Admission routes1
Has abstractyes

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