{"id":"W4408324534","doi":"10.1109/globecom52923.2024.10901551","title":"Predictive Modeling of Resource Utilization in Cloud Data Centers Using Multi-Output Regression","year":2024,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cloud computing; Data modeling; Regression analysis; Regression; Resource (disambiguation); Data mining; Machine learning; Statistics; Database; Operating system; Mathematics","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":[0.001134068,0.0009963082,0.0008330125,0.0008036626,0.0003669772,0.0009905037,0.0009292929,0.0006031587,0.0005998665],"category_scores_gemma":[0.002958162,0.0003241042,0.000781632,0.001200318,0.0002859748,0.001078913,0.000490526,0.001201488,0.0002157539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008104476,"about_ca_system_score_gemma":0.0007037521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01521504,"about_ca_topic_score_gemma":0.008692574,"domain_scores_codex":[0.9994764,0.0001416326,0.00002835638,0.0001484938,0.0001431293,0.00006206018],"domain_scores_gemma":[0.9991447,0.0004776278,0.0001237343,0.00007689076,0.000153921,0.00002317634],"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":[0.00004998509,0.00006696014,0.00379571,0.00003157051,0.00003529349,0.0000575157,0.00002431488,0.9712374,0.001309313,0.0008342201,0.0002853819,0.02227235],"study_design_scores_gemma":[5.698114e-7,0.000004151343,0.0002631088,0.000001017715,0.000001850659,0.000002531178,0.000002681646,0.9993306,0.0002120926,0.0001522586,0.00002763837,0.000001506499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3016889,0.0006048298,0.6922018,0.0004670314,0.00006945431,0.0001081701,0.0004439939,0.001915612,0.002500216],"genre_scores_gemma":[0.9721244,0.0002862561,0.02641361,0.00003260904,0.00002263612,0.00005447738,0.0002623777,0.0000511791,0.0007525305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01521504,"threshold_uncertainty_score":0.03025293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1340907989950744,"score_gpt":0.326270265216334,"score_spread":0.1921794662212596,"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."}}