{"id":"W1631664135","doi":"10.1109/iwcmc.2015.7289173","title":"Task filtering as a task admission control policy in cloud server pools","year":2015,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Task (project management); Computer science; Cloud server; Cloud computing; Admission control; Control (management); Scheme (mathematics); Real-time computing; Term (time); Load balancing (electrical power); Task analysis; Offered load; Distributed computing; Computer network; Throughput; Artificial intelligence; Operating system; Engineering; Quality of service","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":[],"consensus_categories":[],"category_scores_codex":[0.00061904,0.0001602964,0.0001919046,0.0002153029,0.00006931718,0.0001902957,0.0008572497,0.00005692076,0.00001441841],"category_scores_gemma":[0.0001339993,0.0001284681,0.00006361108,0.0005403621,0.0000168819,0.00004922579,0.0008027677,0.0001412104,0.0002354929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001254681,"about_ca_system_score_gemma":0.000121125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001102366,"about_ca_topic_score_gemma":0.00002934112,"domain_scores_codex":[0.9984035,0.0001114136,0.0002632972,0.000405466,0.0003561566,0.0004600919],"domain_scores_gemma":[0.998965,0.0000547376,0.00006661413,0.0005894526,0.00004097864,0.000283235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000243789,0.001233381,0.008020602,0.0001895579,0.0002101794,0.001131824,0.0168148,0.2622567,0.004185941,0.3149047,0.120882,0.2699265],"study_design_scores_gemma":[0.004673662,0.0003423745,0.003743871,0.000182187,0.000009621537,0.00006749418,0.0003270923,0.8122026,0.0005013692,0.01112024,0.1661371,0.0006923852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6760476,0.0001956414,0.2417418,0.02399285,0.0009332535,0.0005663845,0.000001290585,0.000760157,0.05576104],"genre_scores_gemma":[0.9886183,0.000001210508,0.002209553,0.002754327,0.0003050943,0.000008283452,5.860456e-7,0.0000100013,0.006092617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.549946,"threshold_uncertainty_score":0.5238779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01758175814791712,"score_gpt":0.2608309179414589,"score_spread":0.2432491597935417,"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."}}