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Record W2117890135 · doi:10.1109/imtc.2005.1604593

Packet Loss Measurement and Control for VPN based Services

2005· article· en· W2117890135 on OpenAlexaff
Dongli Zhang, Dan Ionescu

Bibliographic record

Venue2005 IEEE Instrumentationand Measurement Technology Conference Proceedings · 2005
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPacket lossComputer networkComputer scienceBandwidth throttlingQuality of serviceNetwork packetProvisioningService providerController (irrigation)Packet switchingEnd-to-end delayService (business)Real-time computingEngineering

Abstract

fetched live from OpenAlex

Provisioning QoS enabled VPN services over packet switched networks is increasingly important for service providers. Prior works usually adopted the proactive service admission approach, but little attention has been given to the control of QoSetersparameters after the service has been instantiated. This paper proposes a packet loss measurement and rate-based feedback control system that maintains preset packet loss targets for instantiated VPN services in the provider's backbone network. Specifically, the system utilizes the measurement and estimation of packet loss probability as the feedback signal, and then applies a pole placement technology to design the controller for throttling ingress customers' traffic. Through a number of experiments, the transient and steady state performance of the controller is evaluated. The numeric results show that, under appropriately selected control gains, it is possible to maintain the network operation within a prescribed loss range.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.023
GPT teacher head0.227
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2005
Admission routes1
Has abstractyes

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