{"id":"W3149220685","doi":"10.1186/s13677-021-00238-6","title":"DRMaestro: orchestrating disaggregated resources on virtualized data-centers","year":2021,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 European Research Council; Institució Catalana de Recerca i Estudis Avançats; Generalitat de Catalunya; European Commission; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Provisioning; Computer science; Workload; Data center; Distributed computing; Software deployment; Scalability; Resource (disambiguation); Quality of service; Resource allocation; Computer network; Database; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008668323,0.0001907394,0.000385791,0.000104103,0.0004147161,0.0004689838,0.001158254,0.00004667181,6.933761e-7],"category_scores_gemma":[0.00007944375,0.0001582611,0.00008022201,0.0005064825,0.00006562518,0.00009552266,0.0005828821,0.0002970384,0.000003611211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000406186,"about_ca_system_score_gemma":0.000066318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001099072,"about_ca_topic_score_gemma":0.000001496303,"domain_scores_codex":[0.9977751,0.0002185408,0.0008192575,0.0004652909,0.0004546064,0.0002671477],"domain_scores_gemma":[0.9973804,0.0004555724,0.000943856,0.0008332257,0.0002273981,0.0001595103],"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":[0.00004721064,0.0007952363,0.002147613,0.0005139065,0.0004110487,0.0002177531,0.002217036,0.370425,0.001055347,0.1337929,0.002637416,0.4857395],"study_design_scores_gemma":[0.001468111,0.0002886829,0.0005449811,0.001264268,0.00006539695,0.0008828362,0.0022363,0.5434155,0.0001784615,0.001048518,0.4481296,0.0004773173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2748087,0.009532785,0.7106644,0.001492389,0.001080032,0.0003033318,0.000008489001,0.0001571208,0.001952742],"genre_scores_gemma":[0.9829895,0.0001256017,0.01526999,0.0001767551,0.001203982,0.000004656855,0.000004850229,0.00001589265,0.0002088063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7081807,"threshold_uncertainty_score":0.64537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02409135462519495,"score_gpt":0.2865590153364415,"score_spread":0.2624676607112465,"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."}}