{"id":"W2402509877","doi":"10.1109/bigdataservice.2016.16","title":"Policy-Based QoS Enforcement for Adaptive Big Data Distribution on the Cloud","year":2016,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Quality of service; Cloud computing; Computer science; Overhead (engineering); Big data; Scheme (mathematics); Distributed computing; Computer network; Mobile QoS; Service (business); Data mining; Service provider; Operating system; Mathematics","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":[0.002741474,0.0003719249,0.0004946023,0.0005682381,0.001135974,0.001518548,0.001450775,0.0005785255,0.0007951276],"category_scores_gemma":[0.006012808,0.0002583483,0.0002326414,0.0006378071,0.0007990817,0.001642472,0.001099828,0.001236117,0.0001914727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001404498,"about_ca_system_score_gemma":0.001976827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002684426,"about_ca_topic_score_gemma":0.002551108,"domain_scores_codex":[0.9982902,0.000447601,0.0001538002,0.0003383453,0.0005398589,0.0002302989],"domain_scores_gemma":[0.9961277,0.001376207,0.0004952682,0.000750552,0.000815903,0.0004343792],"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":[0.001095913,0.0008258781,0.01291437,0.0002596387,0.0001329984,0.0007209096,0.0007574806,0.4782254,0.08483954,0.09629219,0.009951053,0.3139847],"study_design_scores_gemma":[0.00002398955,0.00003333135,0.000467796,0.000005966324,0.000007009936,0.00006655277,0.00005489188,0.9846874,0.006759055,0.006256653,0.001623721,0.00001364128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08045235,0.0003365403,0.912954,0.0007811794,0.000212259,0.0001711527,0.00005145847,0.00216166,0.00287937],"genre_scores_gemma":[0.9306912,0.0001331815,0.06805612,0.000177305,0.00006091292,0.00005195343,0.0000422045,0.00006684237,0.0007203818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002741474,"threshold_uncertainty_score":0.01449847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07807632973415693,"score_gpt":0.2732533650264922,"score_spread":0.1951770352923353,"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."}}