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Record W2245716219 · doi:10.5555/2984075.2984077

DynFilter: limiting bandwidth of online games using adaptive pub/sub message filtering

2015· article· en· W2245716219 on OpenAlex
Julien Gascon‐Samson, Jörg Kienzle, Bettina Kemme

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

VenueNetwork and System Support for Games · 2015
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceBandwidth (computing)ProvisioningLimitingComputer networkServerBandwidth allocationDynamic bandwidth allocationDistributed computing

Abstract

fetched live from OpenAlex

Multiplayer online games can generate a lot of server-related outgoing bandwidth, due to many factors such as highly variable amounts of players or the gathering of many players towards the same in-game locations. Predicting the exact amount of required bandwidth to support varying conditions can be costly, and players can experience game-wide failures if bandwidth is insufficiently provisioned. We present DynFilter, a game-oriented message processing middleware designed to adaptively filter state update messages for in-game entities located apart, in order to reduce bandwidth needs and stay within predefined quotas. We ran experiments on Amazon EC2 over a prototype game mimicking a FPS and a MMOG. Our results show that DynFilter is properly able to maintain bandwidth use within the pre-established quotas while still maintaining adequate delivery of relevant state update messages.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.051
GPT teacher head0.267
Teacher spread0.216 · 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