{"id":"W2315595044","doi":"10.1002/nem.1928","title":"ROUTE: run‐time robust reducer workload estimation for MapReduce","year":2016,"lang":"en","type":"article","venue":"International Journal of Network Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Workload; Reducer; Scheduling (production processes); Distributed computing; Real-time computing; Mathematical optimization; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001218283,0.001058456,0.0007080569,0.0006685947,0.0003764365,0.0007208044,0.001675387,0.0005156354,0.002116216],"category_scores_gemma":[0.004267843,0.0004535559,0.0005353581,0.0004915056,0.0004063209,0.000860198,0.0008603041,0.001065985,0.001319884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004613197,"about_ca_system_score_gemma":0.001424014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006118317,"about_ca_topic_score_gemma":0.004545044,"domain_scores_codex":[0.9989091,0.0001978755,0.00004874546,0.0002813826,0.0004868305,0.00007609937],"domain_scores_gemma":[0.9984677,0.000380467,0.000174963,0.0004142039,0.0004578947,0.0001046581],"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":[0.0005714917,0.0001673421,0.004455748,0.0002939913,0.0001979104,0.0002700327,0.0002230225,0.7154675,0.04022948,0.00393004,0.02135898,0.2128346],"study_design_scores_gemma":[0.00001863847,0.00005040082,0.0004679078,0.000004791624,0.000007691927,0.00004972999,0.0000205443,0.9870996,0.008410854,0.00107597,0.002771008,0.00002284335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06174235,0.0006305351,0.8897126,0.0002805582,0.0001795665,0.0001870618,0.0007388894,0.04471587,0.001812484],"genre_scores_gemma":[0.5978717,0.0002427025,0.3946502,0.0001471988,0.0001123562,0.0001927928,0.002065053,0.002188757,0.002529319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006118317,"threshold_uncertainty_score":0.01216543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270767268784854,"score_gpt":0.2445965335879856,"score_spread":0.2318888609001371,"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."}}