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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 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

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
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