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Record W2147761933 · doi:10.1109/aina.2007.61

Dynamic Bandwidth Allocation in SIP based MPLS

2007· article· en· W2147761933 on OpenAlex
Mohamed El Hachimi, Bernard Tremblay, Michel Kadoch, Maria Bennani

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

VenueProceedings · 2007
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMultiprotocol Label SwitchingComputer networkQuality of serviceComputer scienceReservationBandwidth (computing)Bandwidth allocationDynamic bandwidth allocationAdmission controlResource Reservation ProtocolSignaling protocolResource allocationPrioritizationCall Admission ControlResource management (computing)Internet protocol suiteThe InternetTelecommunicationsWireless

Abstract

fetched live from OpenAlex

With the migration of real-time applications such as voice onto IP-based platforms, the existing IP network capabilities become inadequate to provide the quality-of- service (QoS) levels that the end-users are accustomed to. While new protocols such as DiffServ and MPLS allow some amount of traffic prioritization, guaranteed QoS requires admission control and dynamic resource management. Over-reservation has the advantage of infrequent variations but leads to wastage of resources. Under- reservation, on the other hand, does not meet the QoS expectations of the user flows. In this paper, we consider the architecture based on SIP (Session Initiation Protocol) over MPLS to provide control admission. In this architecture, we propose a new method to reserve optimally the bandwidth of an LSP (Label Switched Path), avoiding an excess of bandwidth renegotiations on the basis of prediction of future traffic.

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 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 score0.968
Threshold uncertainty score0.353

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 communication0.0000.000
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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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