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FUDP: AN SDN-BASED MECHANISM FOR CONTROLLING UDP FLOWS

2018· article· en· W2891242934 on OpenAlexvenueno aff
Hui Hu, Bo Liu, Chao Hu, Ming Chen, Guang Cheng

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2018
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsnot available
FundersGovernment of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsMechanism (biology)Computer scienceComputer networkPhysics

Abstract

fetched live from OpenAlex

It is always a complicated problem to effectively manage and control UDP traffic on the Internet. As UDP traffic is still the chief component of the Internet, the Internet would become difficult to operate stably once network congestion had occurred. Therefore, it is necessary to explore effective control mechanisms for UDP traffic under the current IP framework. This paper introduces the concept of friendly-UDP (FUDP), which aims to give UDP flows quasi-TCP-friendly characteristics. A FUDP mechanism is designed based on software-defined networking (SDN), which controls UDP traffic through flows in a closed-loop manner. The experimental results on the test bed and real Internet trace-driven analysis show that the FUDP mechanism enables UDP to have FUDP features, which avoids network congestion in bottlenecks and allows the whole network to achieve higher throughput and greater bandwidth fairness.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score0.215

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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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