{"id":"W2749299494","doi":"10.1186/s13677-017-0087-y","title":"Burstiness-aware service level planning for enterprise application clouds","year":2017,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Burstiness; Computer science; Cloud computing; Workload; Service level; Service-level agreement; Service (business); Plan (archaeology); Distributed computing; Resource allocation; Resource (disambiguation); Computer network; 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":[0.0008201901,0.0001906743,0.0003669808,0.00009888719,0.001263989,0.0006044098,0.001504805,0.00006008875,1.339737e-7],"category_scores_gemma":[0.00004056244,0.0001648892,0.0001013561,0.0001531559,0.00005179764,0.0001217602,0.000390303,0.0001752147,0.00000221983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004346305,"about_ca_system_score_gemma":0.00004390103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002987346,"about_ca_topic_score_gemma":0.000002244046,"domain_scores_codex":[0.9983696,0.00003962225,0.0006886664,0.0003512144,0.0002837993,0.0002671292],"domain_scores_gemma":[0.9965805,0.0002593516,0.001766277,0.0007525712,0.0005059569,0.0001353316],"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":[0.00004464728,0.0002658983,0.005426447,0.00111119,0.0001740668,0.000009390602,0.001608094,0.3312279,0.0003447252,0.08302375,0.00108402,0.5756798],"study_design_scores_gemma":[0.000972837,0.0001172188,0.003237758,0.0005753037,0.00004670614,0.0001778883,0.0005027243,0.8068028,0.00004649085,0.003201974,0.1839986,0.0003196292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03301623,0.002094175,0.9620044,0.001201612,0.0007587679,0.0005358476,0.000005752011,0.00006771936,0.00031553],"genre_scores_gemma":[0.9807051,0.00002317187,0.01711415,0.0001073213,0.001882004,0.00004709561,0.000001586732,0.00001602464,0.0001034878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9476889,"threshold_uncertainty_score":0.9721712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03374864244092402,"score_gpt":0.3135425792137882,"score_spread":0.2797939367728642,"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."}}