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Record W1983133911 · doi:10.1109/iit.2007.4430446

Bottleneck Analysis of Traffic Monitoring using Wireshark

2007· article· en· W1983133911 on OpenAlex
Abes Dabir, Ashraf Matrawy

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceNetwork packetBottleneckEthernetKernel (algebra)Real-time computingLocal area networkComputer networkLinux kernelOperating systemEmbedded systemPacket analyzer

Abstract

fetched live from OpenAlex

This paper looks at the bottlenecks associated with packet capturing using commodity hardware in local area networks (LANs) without losing data. Experiments were carried out using the Wireshark packet sniffer to write captured packets directly to disk in a Fast Ethernet network with various test setups. These experiments involved generating large packets at almost line rate. Various sizes of the kernel level buffer associated with the packet capturing socket were also experimented with. As well, a simple multithreaded design with user level buffers was proposed for the capturing application and experiments were carried out with this solution. The results showed that increasing the buffering at either the kernel level or the application level can significantly improve capturing performance. The best results can be achieved by using a mix of increased kernel socket buffering and a multithreaded capturing application with its own store and hold buffers.

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)0.000
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: none
Teacher disagreement score0.688
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.023
GPT teacher head0.275
Teacher spread0.252 · 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

Quick stats

Citations47
Published2007
Admission routes1
Has abstractyes

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