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Record W4385841889 · doi:10.5121/ijcnc.2023.15401

Enhancing HTTP Web Protocol Performance with Updated Transport Layer Techniques

2023· article· en· W4385841889 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational journal of Computer Networks & Communications · 2023
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of the Fraser Valley
FundersUniversity of Aberdeen
KeywordsComputer scienceComputer networkTransmission Control ProtocolTransport layerNetwork congestionInternet protocol suiteTCP Friendly Rate ControlProtocol stackHypertext Transfer ProtocolTCP tuningThe InternetApplication layerInternet ProtocolLayer (electronics)Operating system

Abstract

fetched live from OpenAlex

Popular Internet applications such as web browsing, and web video download use HTTP protocol as application over the standard Transport Control Protocol (TCP). Traditional TCP behavior is unsuitable for this style of application because their transmission rate and traffic pattern are different from conventional bulk transfer applications. Previous works have analyzed the interaction of these applications with the congestion control algorithms in TCP and the proposed Congestion Window Validation (CWV) as a solution. However, this method was incomplete and has been shown to present drawbacks. This paper focuses on the ‘newCWV’ which was designed to address these drawbacks. NewCWV provides a practical mechanism to estimate the available path capacity and suggests a more appropriate congestion control behavior. This paper describes how this algorithm was implemented in the Linux TCP/IP stack and tested by experiments, where results indicate that, with newCWV, the browsing can get 50% faster in an uncongested network.

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.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0040.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.284
Teacher spread0.267 · 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