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Record W2119346101 · doi:10.1109/glocom.2010.5683124

Mitigating SIP Overload Using a Control-Theoretic Approach

2010· article· en· W2119346101 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRetransmissionServerComputer scienceComputer networkInformation overloadUpstream (networking)Downstream (manufacturing)Reliability (semiconductor)Distributed computingEngineering

Abstract

fetched live from OpenAlex

Retransmission mechanism helps SIP maintain its reliability, but it can also make an overload worse. Recent server collapses due to emergency-induced call volume in carrier networks indicate that the built-in overload control mechanism cannot handle overload conditions effectively. Since the retransmissions caused by the overload are redundant, we suggest mitigating the overload by controlling redundant message ratio to an acceptable level. Using control-theoretic approach, we model the interaction of an overloaded downstream server with its upstream server as a feedback control system. Then we develop an adaptive PI control algorithm to mitigate the overload at the downstream server by controlling the retransmission message rate of its upstream servers. By performing OPNET simulations on two typical overload scenarios, we demonstrate that: (1) without overload control algorithm applied, the overload at the downstream server may propagate to its upstream servers; (2) our control-theoretic solution not only mitigate the overload effectively, but also achieve a satisfactory target redundant message ratio.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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