MétaCan
Menu
Back to cohort
Record W2169497251 · doi:10.1109/ccgrid.2006.47

Gang scheduling and adaptive resource allocation to mitigate advance reservation impact

2006· article· en· W2169497251 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReservationComputer scienceScheduling (production processes)Distributed computingScheduleGridShared resourceProcessor schedulingMatching (statistics)Computer multitaskingComputer networkParallel computingOperating systemEngineeringOperations management

Abstract

fetched live from OpenAlex

Simultaneous parallel computational grid jobs require reservation by the local job schedulers to ensure allocation of matching time slots at the different sites involved. However, reservations create road blocks in the local schedule, leading to only a small percentage of reservations being tolerable. A large number of reservations typically has adverse effects on local response times and machine utilization. We have extended our SCOJO scheduler to enable advance reservations. SCOJO can perform space sharing or gang scheduling and can run as either adaptive or traditional non-adaptive variant. We show that gang scheduling is more flexible than space sharing in regards to tolerating reservations. We also show that, for space sharing and a low multiprogramming level, the adaptive variants can tolerate reservations better than the non-adaptive variants.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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