{"id":"W3015884705","doi":"10.1109/cloudnet47604.2019.9064138","title":"Cloud Workload Characterization and Profiling for Resource Allocation","year":2019,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Cloud computing; Computer science; Profiling (computer programming); Workload; Scheduling (production processes); Revenue; Resource allocation; Cloud service provider; Cluster analysis; Service provider; Distributed computing; Service (business); Computer network; Cloud computing security; Operations management; Business; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.000253802,0.00006812142,0.00007280532,0.00004867958,0.00007615796,0.0001249186,0.0002146627,0.00002883525,0.000002361774],"category_scores_gemma":[0.00001020473,0.00005869729,0.00002157086,0.0001320468,0.000007378613,0.00002766719,0.0001635516,0.00003531655,0.0000189937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001279075,"about_ca_system_score_gemma":0.00000725122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000211618,"about_ca_topic_score_gemma":3.091636e-7,"domain_scores_codex":[0.9993562,0.00001976789,0.0001176945,0.0002698584,0.00009822592,0.0001382482],"domain_scores_gemma":[0.9995835,0.00004916451,0.00005332378,0.0002525106,0.00002996926,0.00003152611],"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":[0.00003050626,0.00007073636,0.00472028,0.000161817,0.00003310818,7.145224e-7,0.001147932,0.002607243,0.01411424,0.2089211,0.0004713483,0.767721],"study_design_scores_gemma":[0.0003833733,0.0001036859,0.002725119,0.0000480025,0.000004264003,0.00000235573,0.00006791533,0.9457665,0.003164433,0.0003838178,0.04718895,0.0001615545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4718292,0.0000197448,0.5249262,0.001282605,0.0001642094,0.0003185549,1.751658e-7,0.0001314161,0.001327876],"genre_scores_gemma":[0.9707119,0.000002459861,0.01979366,0.0006506294,0.0001572003,0.00001762229,0.000005576531,0.000007806218,0.008653082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9431593,"threshold_uncertainty_score":0.2393606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068508278467512,"score_gpt":0.2191438761290063,"score_spread":0.2084587933443312,"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."}}