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Scheduler Design for Mobility-aware Multipath QUIC

2022· article· en· W4315606013 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

VenueGLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGoodputMultihomingComputer scienceComputer networkMobility modelMultipath propagationNetwork packetMultipath TCPScheduling (production processes)Mobility managementDistributed computingWirelessThe InternetInternet ProtocolTelecommunicationsThroughputChannel (broadcasting)

Abstract

fetched live from OpenAlex

Multi-homing technologies are promising to support seamless user mobility, as a mobile device can use multiple access links and paths for non-interrupted transmissions. Scheduling packets across multiple paths, however, has the known issue of out-of-order (OFO) due to the heterogeneity of the paths. Mobility poses new challenges due to time-varying link quality and capacity. In this paper, we present a novel Mobility-aware Multipath Quick UDP Internet Connections (MMQUIC) framework which enables collaboration between the link and transport layer. Based on MMQUIC, we develop a Mobility-Aware Multipath Scheduler (MAMS) for goodput enhancement, in which the impacts of mobility such as link outage errors and capacity variations are considered. Finally, we evaluate the performance of MAMS using network simulator 3 (ns-3). Simulation results demonstrate that our design has substantial performance gains with respect to the goodput and packet delay distribution in dynamic wireless systems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0090.004
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 head0.096
GPT teacher head0.334
Teacher spread0.238 · 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