{"id":"W4230608576","doi":"10.32920/ryerson.14645862.v1","title":"Resource allocation and task admission control in cloud systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"University of Toronto","keywords":"Cloud computing; Computer science; Blocking (statistics); Task (project management); Resource allocation; Resource (disambiguation); Distributed computing; Real-time computing; Admission control; Prioritization; Service (business); Response time; Computer network; Quality of service; Operating system; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001274135,0.0006226897,0.0008343013,0.0005100894,0.0008683756,0.002219722,0.001212117,0.0006563437,0.001242007],"category_scores_gemma":[0.004773872,0.0002801613,0.0004110007,0.0008306646,0.0008836666,0.001408703,0.0009929319,0.0009639458,0.00018658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001814213,"about_ca_system_score_gemma":0.002739436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01112198,"about_ca_topic_score_gemma":0.004531472,"domain_scores_codex":[0.9984256,0.0004419628,0.0000960332,0.0002542573,0.0004501863,0.0003319741],"domain_scores_gemma":[0.998359,0.0008564654,0.000242834,0.0001742489,0.0002589474,0.0001084677],"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":[0.000170518,0.00007676075,0.001135193,0.0001001026,0.00003510488,0.00008243477,0.0001375074,0.9091189,0.007319591,0.03683156,0.0009127892,0.04407962],"study_design_scores_gemma":[0.000006050038,0.00001579097,0.000119317,0.000004835509,0.000004201455,0.000009471937,0.00001630985,0.9942163,0.0006990226,0.004525192,0.0003783375,0.00000516778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08857323,0.001393891,0.9029773,0.0005497242,0.0001262098,0.0001237034,0.00005853083,0.000594902,0.00560248],"genre_scores_gemma":[0.9662699,0.0005200846,0.03126052,0.00007509243,0.00007252426,0.00007630872,0.00003222246,0.00003719962,0.001656208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01112198,"threshold_uncertainty_score":0.02211452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01101798088325005,"score_gpt":0.2261809994587643,"score_spread":0.2151630185755143,"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."}}