{"id":"W2153436173","doi":"10.1109/ccgrid.2012.47","title":"Optimal Reconfiguration of the Cloud Network for Maximum Energy Savings","year":2012,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Provisioning; Cloud computing; Computer science; Heuristics; Distributed computing; Energy consumption; Data center; Server; Cloudlet; Integer programming; Computer network; Operating system; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004546884,0.00006627754,0.00007883544,0.00001657006,0.0001226632,0.00003345245,0.0005166801,0.00002873603,0.00000778105],"category_scores_gemma":[0.00001554157,0.0000419751,0.00008249946,0.0001674209,0.00001884269,0.00002382979,0.0002325459,0.00003189607,0.000002521342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001424111,"about_ca_system_score_gemma":0.000009605785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003860374,"about_ca_topic_score_gemma":0.000003864166,"domain_scores_codex":[0.9993157,0.00004394448,0.0001563438,0.0001232588,0.0001186576,0.00024207],"domain_scores_gemma":[0.9993961,0.00009279614,0.00009449624,0.0003476688,0.0000339019,0.00003504941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008537102,0.00009401947,0.0008344956,0.00002089354,0.00004018155,1.176874e-7,0.000794598,0.08082314,0.0001732425,0.6637201,0.05147924,0.2020115],"study_design_scores_gemma":[0.0003461467,0.00008294135,0.003210712,0.00003600274,0.00001643469,0.000006071082,0.00006615232,0.6525927,0.004026963,0.005672106,0.3337291,0.0002145739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04293777,0.00009919611,0.9443083,0.001378112,0.001422036,0.0001101888,2.630546e-7,0.00006786024,0.009676216],"genre_scores_gemma":[0.9485075,0.00000107534,0.04697448,0.0005383494,0.0006661203,0.000009230604,4.816675e-7,0.000005136919,0.003297596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9055697,"threshold_uncertainty_score":0.1711695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334201777479501,"score_gpt":0.2149447433806295,"score_spread":0.2016027256058345,"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."}}