{"id":"W3159911834","doi":"10.1109/access.2021.3075973","title":"DECA: A Dynamic Energy Cost and Carbon Emission-Efficient Application Placement Method for Edge Clouds","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"European Commission","keywords":"Deca-; Computer science; Energy consumption; Carbon fibers; Enhanced Data Rates for GSM Evolution; Efficient energy use; Greenhouse gas; Energy (signal processing); Cloud computing; Algorithm; Electrical engineering; Telecommunications; Engineering; Mathematics; Operating system","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":[0.0003678218,0.0001424713,0.0001645631,0.00007032209,0.0001804674,0.0002480281,0.0006441943,0.00005321084,0.000001096104],"category_scores_gemma":[0.000020438,0.0001300138,0.00005269773,0.0003643969,0.00001791624,0.000016643,0.0005824656,0.0000637863,6.505022e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009012922,"about_ca_system_score_gemma":0.00005248615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000081374,"about_ca_topic_score_gemma":0.00002204519,"domain_scores_codex":[0.998622,0.00007527023,0.0002172956,0.0005901972,0.0002215508,0.0002736891],"domain_scores_gemma":[0.9989651,0.000180633,0.000105234,0.0005409914,0.0001008585,0.0001071277],"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":[0.00002813755,0.0003508649,0.0001869783,0.0001517014,0.00008628528,0.00002073766,0.0008157284,0.2069982,0.006047189,0.007276572,0.001714541,0.7763231],"study_design_scores_gemma":[0.0004228101,0.00002465572,0.000155852,0.00002975072,0.00001627127,0.0000100364,0.00003846949,0.9724807,0.009467979,0.0005782847,0.01660936,0.0001658634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09090575,0.0003139801,0.9062982,0.000929774,0.0004719551,0.0002759625,0.000001701449,0.00009806248,0.0007046303],"genre_scores_gemma":[0.9767236,0.00001474101,0.02161372,0.000548846,0.0001224453,0.0002053887,0.000005915969,0.00001510015,0.0007502766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8858178,"threshold_uncertainty_score":0.5301811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01645128167086935,"score_gpt":0.3122810633508952,"score_spread":0.2958297816800259,"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."}}