MétaCan
Menu
Back to cohort
Record W4413797267 · doi:10.55248/gengpi.6.0825.3072

High IO Performance User Space Networking with eBPF/XDP and AF_XDP: Design, Tuning, and Architectural Insights

2025· article· en· W4413797267 on OpenAlexaff
Manish Kumar

Bibliographic record

VenueInternational Journal of Research Publication and Reviews · 2025
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsComputer scienceSpace (punctuation)Operating system

Abstract

fetched live from OpenAlex

The growth of edge computing, 5G deployments, and high IO services has intensified the demand for packet processing methods that combine raw throughput with operational stability.Traditional kernel-bypass approaches can deliver speed but often sacrifice integration with Linux's networking ecosystem.This paper presents eBPF/XDP with AF_XDP zero-copy mode in Linux 6.4 as a high-performance, kernel-integrated alternative.We outline its architecture, show how it eliminates key bottlenecks in the standard kernel data path, and detail proven tuning strategies for achieving line-rate user space I/O.Implementation examples are provided from both kernel and user space perspectives, supported by performance-oriented design practices.eBPF/XDP is a methodology for building low-latency and highthroughput applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.060
GPT teacher head0.359
Teacher spread0.299 · 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 designOther design
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Research Publication and ReviewsSame topicIoT and Edge/Fog ComputingFrench-language works237,207