{"id":"W2972125603","doi":"10.1109/tii.2019.2939573","title":"A Big Data-Enabled Consolidated Framework for Energy Efficient Software Defined Data Centers in IoT Setups","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Office of Advanced Cyberinfrastructure; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Cloud computing; Big data; Energy consumption; Distributed computing; Server; Efficient energy use; Data center; Quality of service; Software deployment; Computer network; Operating system; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002519855,0.001010127,0.0007941754,0.0008557942,0.0009361463,0.003753559,0.003656113,0.001173971,0.002472228],"category_scores_gemma":[0.002291339,0.0006298213,0.001189458,0.0009405275,0.001383106,0.003370306,0.004186281,0.002103371,0.0006420164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0017344,"about_ca_system_score_gemma":0.003176728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009047579,"about_ca_topic_score_gemma":0.01320145,"domain_scores_codex":[0.9987831,0.00033292,0.0000964296,0.0002090762,0.0003995931,0.0001789327],"domain_scores_gemma":[0.9991257,0.0001689969,0.00007028184,0.0002036939,0.0002539757,0.000177467],"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":[0.0001428759,0.0001313814,0.001603172,0.0001798065,0.00008160302,0.0002733319,0.0003091762,0.6671791,0.004192578,0.2787049,0.006035688,0.04116642],"study_design_scores_gemma":[0.00001176437,0.000020774,0.00007765013,0.00001668894,0.00001215814,0.00002511723,0.00003993607,0.9728491,0.0005722229,0.01822222,0.008141134,0.00001130879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006908283,0.0003813532,0.986276,0.0004038358,0.0001092939,0.0001676672,0.0001640516,0.001711521,0.003878054],"genre_scores_gemma":[0.4204228,0.0008967503,0.5729108,0.0002510331,0.0001163939,0.0005335101,0.0007560899,0.0005502217,0.003562381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009047579,"threshold_uncertainty_score":0.01798981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09497985002101941,"score_gpt":0.2733959004091984,"score_spread":0.178416050388179,"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."}}