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Record W4229579902 · doi:10.1145/2637364.2592015

Randomized routing schemes for large processor sharing systems with multiple service rates

2014· article· en· W4229579902 on OpenAlex
Arpan Mukhopadhyay, Ravi R. Mazumdar

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

VenueACM SIGMETRICS Performance Evaluation Review · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAdvanced Queuing Theory Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsServerRouting (electronic design automation)Computer scienceDistributed computingScheme (mathematics)Processor sharingExponential distributionHomogeneousRandomized algorithmComputer networkMathematicsAlgorithmStatisticsQueueing theory

Abstract

fetched live from OpenAlex

We consider randomized job routing techniques for a system consisting of a large number of parallel processor sharing servers with heterogeneous server speeds. In particular, a scheme, that routes an incoming job request to the server providing the highest instantaneous processing rate per job among two servers, chosen uniformly at random, is proposed. We show that, unlike the homogeneous case, in the heterogeneous case, such randomized dynamic schemes need not always perform better than the optimal static scheme (in which jobs are assigned to servers with fixed probabilities independent of server states) in terms of reducing the mean response time of jobs. Specifically, we show that the stability region under the proposed scheme is a subset of that under the optimal static routing scheme. We also obtain the stationary tail distribution of server occupancies for the proposed scheme in the limit as the system size grows to infinity. This distribution has been shown to be insensitive to job length distribution and decay super-exponentially.

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)0.017
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.806
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.055
GPT teacher head0.321
Teacher spread0.266 · 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