{"id":"W3009500028","doi":"10.1109/ccece53047.2021.9569163","title":"LAWA: Loss-Aware Workload Assignment in Data Centers","year":2021,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Research and Development; Science and Engineering Research Council","keywords":"Workload; Server; Computer science; Power consumption; Power demand; Server farm; Power (physics); Computer network; Data center; Consolidation (business); Real-time computing; Operating system; Database; Client–server model","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":[0.002481539,0.001057891,0.0008350222,0.0009333632,0.001079637,0.001531454,0.002986129,0.0006410937,0.002330164],"category_scores_gemma":[0.007123142,0.0006021527,0.000510372,0.0006402431,0.0006620106,0.002179472,0.002132464,0.001013986,0.001177355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009924755,"about_ca_system_score_gemma":0.0016222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003143298,"about_ca_topic_score_gemma":0.003202496,"domain_scores_codex":[0.9984018,0.0004378232,0.00009228617,0.0002863775,0.0004779325,0.0003038126],"domain_scores_gemma":[0.997636,0.0006448114,0.0002681105,0.0005648801,0.0005401367,0.0003461464],"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":[0.00159406,0.0009413899,0.01086593,0.0002113916,0.0001000379,0.0002387842,0.0003440189,0.4303301,0.03884439,0.00505927,0.0110034,0.5004672],"study_design_scores_gemma":[0.00002910729,0.00006933935,0.0003761112,0.00000346143,0.000006245971,0.00003642779,0.00002879307,0.9936289,0.003875467,0.001278206,0.0006618297,0.000006144974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1059173,0.0003857533,0.8778343,0.000351253,0.0001829654,0.0004389179,0.0001185861,0.01204573,0.002725328],"genre_scores_gemma":[0.7686797,0.000108542,0.2273106,0.0001799611,0.00007677271,0.0002157983,0.000333839,0.0005027712,0.002592036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003143298,"threshold_uncertainty_score":0.01312375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03359547029320746,"score_gpt":0.2616455874552013,"score_spread":0.2280501171619939,"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."}}