{"id":"W3179220440","doi":"10.1109/iccworkshops50388.2021.9473607","title":"Time Series Forecasting using Facebook Prophet for Cloud Resource Management","year":2021,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cistel Technology (Canada); Concordia University","funders":"","keywords":"Cloud computing; Computer science; Workload; Scheduling (production processes); Data center; Resource management (computing); Time series; Autoregressive integrated moving average; Preprocessor; Data pre-processing; Virtual machine; Distributed computing; Database; Data mining; Machine learning; Artificial intelligence; 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":[0.0003887038,0.0001780714,0.0001860914,0.00007490951,0.0003653571,0.0002846121,0.0005736764,0.00004023218,0.00003852347],"category_scores_gemma":[0.00002050121,0.0001613331,0.0001203981,0.0003209233,0.00003858078,0.00004064725,0.001163073,0.00007210853,0.00004425425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006096627,"about_ca_system_score_gemma":0.00002903694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002763031,"about_ca_topic_score_gemma":0.000001242156,"domain_scores_codex":[0.9984035,0.00006251228,0.0002675585,0.0005659701,0.0002632893,0.0004370945],"domain_scores_gemma":[0.9990739,0.00006972389,0.00008771271,0.00061,0.00008065159,0.00007805436],"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":[0.00008370795,0.0004569449,0.00009691023,0.001176253,0.0007042091,0.000931141,0.003645987,0.2295229,0.002104201,0.2315117,0.06826498,0.4615011],"study_design_scores_gemma":[0.0003985893,0.00005706794,0.000009271974,0.00008388823,0.00002325136,0.00007336629,0.0003478737,0.7647911,0.002007513,0.001352098,0.2305831,0.0002728934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0268303,0.0001057418,0.8773417,0.002060908,0.0004290767,0.001094026,0.000001852442,0.0006314249,0.09150495],"genre_scores_gemma":[0.02752881,0.000001998274,0.7837169,0.001235664,0.0005102364,0.00009302331,0.000007462006,0.00004776039,0.1868581],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5352682,"threshold_uncertainty_score":0.6578975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03208335960671745,"score_gpt":0.2358898788792894,"score_spread":0.203806519272572,"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."}}