{"id":"W4381384545","doi":"10.1016/j.suscom.2023.100888","title":"Energy and carbon-aware initial VM placement in geographically distributed cloud data centers","year":2023,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"CloudSim; Cloud computing; Data center; Computer science; Energy consumption; Efficient energy use; Greenhouse gas; Environmental economics; Carbon fibers; Virtual machine; Environmental science; Algorithm; Operating system; Engineering","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":[0.0004657284,0.0003067524,0.000376658,0.0004009244,0.0007059699,0.0008795605,0.0006277798,0.0004620564,0.001746974],"category_scores_gemma":[0.001564484,0.0001740337,0.0001720028,0.0004378686,0.0002885036,0.0006117279,0.0004349053,0.0002503414,0.0001409941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008931434,"about_ca_system_score_gemma":0.001190194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007800887,"about_ca_topic_score_gemma":0.01767953,"domain_scores_codex":[0.9997178,0.00005983356,0.000008814264,0.0000598597,0.0000538356,0.00009985921],"domain_scores_gemma":[0.9994127,0.0002213035,0.00006258363,0.0000484571,0.0001724156,0.00008263121],"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":[0.0008944209,0.000264617,0.00891901,0.0000618073,0.0000311661,0.000184211,0.00006593506,0.9378048,0.01188278,0.002294465,0.001195822,0.03640094],"study_design_scores_gemma":[0.00001293949,0.00006371891,0.002727804,0.0000037167,0.00001100893,0.00002439273,0.0001129177,0.99253,0.003312187,0.001026892,0.0001691845,0.000005161781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474827,0.0003188843,0.04491511,0.0003946662,0.00007406709,0.00006041353,0.0001301794,0.0003198617,0.006304096],"genre_scores_gemma":[0.9962965,0.00001365273,0.003296789,0.000009291902,0.000002742001,0.000003536363,0.00001898623,0.000007892465,0.0003507424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007800887,"threshold_uncertainty_score":0.01551092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156882160483962,"score_gpt":0.2420575950017733,"score_spread":0.2263693789533771,"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."}}