{"id":"W2973192993","doi":"10.1016/j.asoc.2019.105759","title":"SARA: Stably and quickly find optimal cloud configurations for heterogeneous big data workloads","year":2019,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"Scientific and Innovative Action Plan of Shanghai; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Workload; Overhead (engineering); Distributed computing; Big data; Parallel computing; Operating system","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"],"consensus_categories":[],"category_scores_codex":[0.0006821263,0.0002664756,0.0003243821,0.0001055315,0.000464975,0.0004682956,0.00155613,0.00008910438,0.000003480857],"category_scores_gemma":[0.00002944967,0.0002642409,0.00005746467,0.0002939172,0.00005924376,0.00003546098,0.002157377,0.0001966432,0.00004815801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003406415,"about_ca_system_score_gemma":0.00006256284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001723102,"about_ca_topic_score_gemma":0.000003251351,"domain_scores_codex":[0.9976577,0.00003957456,0.0004145168,0.00103207,0.000280778,0.0005753455],"domain_scores_gemma":[0.9976566,0.0005708523,0.0002028648,0.001383295,0.00005909968,0.0001272551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003978014,0.0001418872,0.0003706951,0.0001634864,0.0001882713,0.00001096071,0.002120353,0.2637635,0.001531423,0.03054931,0.002918692,0.6982016],"study_design_scores_gemma":[0.0009374869,0.00009095258,0.0003824524,0.00005129788,0.00002154924,0.00002174433,0.0001461493,0.9765618,0.0001963515,0.000468149,0.02072057,0.0004015238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3513724,0.0001975296,0.6452014,0.0003650541,0.0007764419,0.0006140574,0.000007778284,0.0002682964,0.001197065],"genre_scores_gemma":[0.9382174,0.000002302373,0.06039449,0.0005724262,0.0005139919,0.000009235679,0.0000272629,0.0000258936,0.0002370215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7127982,"threshold_uncertainty_score":0.999981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03411346722654097,"score_gpt":0.2481894759378124,"score_spread":0.2140760087112715,"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."}}