{"id":"W1589728707","doi":"10.1109/tnsm.2015.2440423","title":"Greenslater: On Satisfying Green SLAs in Distributed Clouds","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; École de Technologie Supérieure","funders":"","keywords":"Cloud computing; Carbon footprint; Computer science; Service-level agreement; Service level; Environmental economics; Virtual machine; Profit (economics); Service provider; Greenhouse gas; Service (business); Business","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":[0.002601343,0.0008703135,0.00101178,0.0005514803,0.001769066,0.002079955,0.001765904,0.001063605,0.003102201],"category_scores_gemma":[0.004766123,0.0003311517,0.0006124513,0.001378648,0.001612429,0.003480138,0.003512079,0.001611932,0.0004551345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001564113,"about_ca_system_score_gemma":0.002756664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00817547,"about_ca_topic_score_gemma":0.006532516,"domain_scores_codex":[0.9982657,0.0005803043,0.00005391682,0.00021113,0.0004282978,0.0004607462],"domain_scores_gemma":[0.9984132,0.0007233022,0.0001501446,0.0002752824,0.0002416838,0.0001963094],"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":[0.0005046875,0.0001952249,0.001218019,0.0002964153,0.00005295508,0.0003280771,0.0003263025,0.70717,0.004775869,0.1484658,0.009815281,0.1268515],"study_design_scores_gemma":[0.00005295759,0.0001016787,0.0002220484,0.00002817757,0.00001667903,0.00006505707,0.000109904,0.9438526,0.001024133,0.04730604,0.007203283,0.00001747054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04267727,0.0025502,0.9393861,0.002298587,0.0004356784,0.0001787923,0.0001149551,0.0009797078,0.01137871],"genre_scores_gemma":[0.8831692,0.001687421,0.110275,0.0005443385,0.000255455,0.00009327697,0.0001271106,0.0001675852,0.003680505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00817547,"threshold_uncertainty_score":0.01625574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02267736028749483,"score_gpt":0.2277176334603492,"score_spread":0.2050402731728543,"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."}}