{"id":"W2487448491","doi":"10.1109/icc.2016.7511249","title":"Chronos: Meeting coflow deadlines in data center networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Bottleneck; Scheduling (production processes); Data center; Distributed computing; Cloud computing; Computer network; Service provider; Service (business); Operating system; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003229438,0.0007825941,0.0008093884,0.0007269771,0.001671726,0.001447826,0.002047079,0.0007131127,0.001607243],"category_scores_gemma":[0.005514298,0.0003951869,0.0003981089,0.0007097194,0.0008791123,0.001675324,0.002042408,0.00125665,0.0002455061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001375369,"about_ca_system_score_gemma":0.003348514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004120879,"about_ca_topic_score_gemma":0.003582239,"domain_scores_codex":[0.9986786,0.0004182597,0.00006166919,0.0002314679,0.000386403,0.0002235557],"domain_scores_gemma":[0.9977361,0.0008837132,0.0003208609,0.000265966,0.0003511027,0.0004422276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001340554,0.0002842805,0.003706861,0.0003799859,0.00009116812,0.0003936062,0.0004914506,0.6950645,0.01508268,0.1178265,0.0171298,0.1482086],"study_design_scores_gemma":[0.0001140645,0.0001518947,0.0003039674,0.00001809056,0.00001869713,0.00009483222,0.00007652916,0.9686047,0.004151253,0.01586141,0.01057887,0.00002572855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07742321,0.001263157,0.911727,0.0005542365,0.0004749039,0.0002913589,0.0002361893,0.003080553,0.004949481],"genre_scores_gemma":[0.7854213,0.0005726605,0.2105781,0.0002017699,0.0002129959,0.0003117587,0.0002364172,0.0002578501,0.00220713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004120879,"threshold_uncertainty_score":0.01707911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02597024574875358,"score_gpt":0.2472874544537835,"score_spread":0.2213172087050299,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}