{"id":"W2743384879","doi":"10.1016/j.future.2017.07.009","title":"A Resource Co-Allocation method for load-balance scheduling over big data platforms","year":2017,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"Applied Basic Research Key Project of Yunnan; Deshpande Center for Technological Innovation, Massachusetts Institute of Technology; Key Medical Subjects of Jiangsu Province; Nanjing University; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Big data; Scheduling (production processes); Distributed computing; Load balancing (electrical power); Balance (ability); Resource allocation; Mathematical optimization; Computer network; Data mining","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","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001976115,0.0002894774,0.0003448586,0.0001103728,0.001381217,0.002276855,0.00353343,0.000156968,7.996337e-7],"category_scores_gemma":[0.00004488985,0.0002474795,0.00009377586,0.0001296818,0.00002787646,0.0001736756,0.001127304,0.0001702084,0.00001932688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001251006,"about_ca_system_score_gemma":0.000117827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008043927,"about_ca_topic_score_gemma":0.00002845457,"domain_scores_codex":[0.9972754,0.0001019306,0.0005217994,0.001088924,0.0006125082,0.0003994653],"domain_scores_gemma":[0.9951829,0.0000949897,0.0006005976,0.003774868,0.0002237403,0.0001228624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001933022,0.00009178768,0.00025812,0.0002299228,0.0001810636,0.00001051509,0.001184822,0.2320968,0.0005809261,0.03463983,0.4205221,0.3101849],"study_design_scores_gemma":[0.0005176971,0.00004328102,0.0001864649,0.00005072959,0.000009867917,0.00001447256,0.00001788545,0.6511092,0.00009710935,0.00001865574,0.3477192,0.0002154148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004624366,0.0005379045,0.9538959,0.001726601,0.03791515,0.0007118967,0.00001851055,0.0002669466,0.0003026689],"genre_scores_gemma":[0.05719354,0.000008055201,0.7083327,0.001163093,0.2314473,0.0001169124,0.0002654375,0.00005584718,0.001417135],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4190125,"threshold_uncertainty_score":0.9999977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.074817494176822,"score_gpt":0.3154752948076273,"score_spread":0.2406578006308053,"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."}}