{"id":"W4388383276","doi":"10.1016/j.comnet.2023.110088","title":"Multi-resource predictive workload consolidation approach in virtualized environments","year":2023,"lang":"en","type":"article","venue":"Computer Networks","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Workload; Service-level agreement; Virtualization; Server; Energy consumption; Service provider; Distributed computing; Host (biology); Service level; Data center; Consolidation (business); Computer network; Operating system; Service (business)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006488271,0.0002529555,0.0002802492,0.0002933638,0.0001653312,0.0001883814,0.001112662,0.0001311501,0.000002596311],"category_scores_gemma":[0.000007218707,0.0002485098,0.00009521065,0.001175301,0.00007077525,0.00005312404,0.001473394,0.0003279066,0.0001037991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009157882,"about_ca_system_score_gemma":0.00001202994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007470266,"about_ca_topic_score_gemma":8.85758e-7,"domain_scores_codex":[0.997609,0.000275299,0.000394884,0.0007895139,0.0003386327,0.0005926224],"domain_scores_gemma":[0.9988334,0.000182591,0.000131378,0.0007180392,0.0000116939,0.0001229207],"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":[0.000009002748,0.0001316034,0.00102019,0.000006449678,0.00003044921,0.00002926684,0.0008116871,0.9050496,0.000003460573,0.0003420755,0.003283161,0.08928309],"study_design_scores_gemma":[0.001089982,0.00005353112,0.02258241,0.00007574233,0.000005553562,0.000005087161,0.00004286243,0.969859,0.000003295816,0.0000640792,0.005967567,0.0002508981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0241595,0.0001671586,0.9731834,0.0001907854,0.0005280809,0.0004131798,6.01201e-7,0.0005813418,0.0007759731],"genre_scores_gemma":[0.9353555,0.00003736692,0.06219132,0.0007635325,0.000553748,0.0000791299,0.00003391241,0.00003559663,0.0009498495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9111961,"threshold_uncertainty_score":0.9999967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01783923817551811,"score_gpt":0.2283054835715666,"score_spread":0.2104662453960485,"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."}}