{"id":"W2885194465","doi":"10.4018/978-1-5225-3142-5.ch014","title":"Resource Provisioning and Scheduling of Big Data Processing Jobs","year":2018,"lang":"en","type":"book-chapter","venue":"Advances in data mining and database management book series","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Cloud computing; Provisioning; Big data; Computer science; Scheduling (production processes); Data processing; Resource (disambiguation); Distributed computing; Data science; Database; Operating system; Engineering; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001354608,0.0004843643,0.0005572129,0.0004592271,0.0003221825,0.0003580029,0.003510073,0.00009520719,0.000007899922],"category_scores_gemma":[0.00008139422,0.0004621141,0.00002133392,0.0001569375,0.0004935138,0.001600831,0.02615771,0.0002562969,0.000002965808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002196957,"about_ca_system_score_gemma":0.00004074033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009804262,"about_ca_topic_score_gemma":0.00007187919,"domain_scores_codex":[0.9962805,0.00004707217,0.0007256647,0.002008103,0.0005282456,0.0004104313],"domain_scores_gemma":[0.9946927,0.0001536164,0.0005749457,0.004434689,0.00004492093,0.0000990702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004949685,0.00003071093,0.00007482048,0.00244536,0.00009094195,0.0001534343,0.0006043357,0.0001131165,0.000001617061,0.02488831,0.003465906,0.968082],"study_design_scores_gemma":[0.000405781,0.00008523819,0.000013118,0.005323471,0.00009714193,0.00002431041,0.0006397371,0.04499622,0.000004707148,0.0007885491,0.9470769,0.0005448944],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.000953935,0.239316,0.1766187,0.001510221,0.001322445,0.002212471,0.001760513,0.0007748028,0.5755309],"genre_scores_gemma":[0.001523108,0.03810403,0.8183063,0.0006982979,0.0009328208,0.00002577055,0.004169481,0.000180391,0.1360599],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.967537,"threshold_uncertainty_score":0.999783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04735395957259508,"score_gpt":0.2769045705384887,"score_spread":0.2295506109658936,"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."}}