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Record W1969602909 · doi:10.1109/tst.2013.6522586

On meeting deadlines in datacenter networks

2013· article· en· W1969602909 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

VenueTsinghua Science & Technology · 2013
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer sciencePartition (number theory)Cloud computingDistributed computingLatency (audio)Computer networkNetwork congestionThroughputNetwork packetWirelessOperating system

Abstract

fetched live from OpenAlex

Datacenters have become increasingly important to host a diverse range of cloud applications with mixed workloads. Traditional applications hosted by datacenters are throughput-oriented without delay requirements, but newer generations of cloud applications, such as web search, recommendations, and social networking, typically employ a tree-based Partition-Aggregate structure, which may incur bursts of traffic. As a result, flows in these applications have stringent latency requirements, i.e., flow deadlines need to be met in order to achieve a satisfactory user experience. To meet these flow deadlines, research efforts in the recent literature have attempted to redesign flow and congestion control protocols that are specific to datacenter networks. In this paper, we focus on the new array of deadline-sensitive flow control protocols, thoroughly investigate their underlying design principles, analyze the evolution of their designs, and evaluate the tradeoffs involved in their design choices.

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: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.002
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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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