{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008718516,0.0007625431,0.0006837868,0.003047958,0.0005578041,0.001298888,0.0005184584,0.0004079723,0.0008986699],"category_scores_gemma":[0.003852076,0.0002739169,0.0003825916,0.002360677,0.0001746046,0.001060243,0.000495057,0.0005946783,0.0009928865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005627726,"about_ca_system_score_gemma":0.000789058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002666201,"about_ca_topic_score_gemma":0.002991008,"domain_scores_codex":[0.9988919,0.0002856971,0.000096048,0.0001888377,0.0004077791,0.0001297791],"domain_scores_gemma":[0.9978437,0.0004872295,0.0004066718,0.0004407196,0.0006624523,0.0001591467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006186434,0.0008083484,0.09351724,0.0003339276,0.0001361478,0.0003234044,0.0004870436,0.1185458,0.1966677,0.01260172,0.01054354,0.5654166],"study_design_scores_gemma":[0.00001231271,0.0001108338,0.03658589,0.00005186452,0.00002459183,0.0003131583,0.0002165968,0.9155015,0.03427675,0.00541431,0.007434935,0.00005727072],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1623933,0.0005126774,0.8249471,0.0002845976,0.00006400378,0.0005326529,0.00221843,0.002747568,0.006299722],"genre_scores_gemma":[0.7913656,0.0003060897,0.2033007,0.00009572728,0.00009399883,0.0002658805,0.003018885,0.0002132749,0.001339957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003047958,"threshold_uncertainty_score":0.005301297,"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."}}