{"id":"W4405305567","doi":"10.1109/eecsi63442.2024.10776180","title":"Ksurf: Attention Kalman Filter and Principal Component Analysis for Prediction under Highly Variable Cloud Workloads","year":2024,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Principal component analysis; Kalman filter; Computer science; Cloud computing; Variable (mathematics); Component (thermodynamics); Artificial intelligence; 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":[0.0005499905,0.000145378,0.0001749287,0.0002778489,0.0001916509,0.0004756564,0.0002721681,0.0000591668,0.00001715944],"category_scores_gemma":[0.000004800302,0.0001175327,0.0001445119,0.0008376834,0.00002584311,0.00005342684,0.0003425246,0.00009141897,0.00001429141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006464884,"about_ca_system_score_gemma":0.00001544145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005090855,"about_ca_topic_score_gemma":0.000008521836,"domain_scores_codex":[0.998604,0.00004968785,0.0002613769,0.0005731177,0.0002458584,0.0002659875],"domain_scores_gemma":[0.9993422,0.0001191935,0.00004095618,0.0003721291,0.00004313068,0.00008236845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003102656,0.0002782057,0.008133778,0.0003687015,0.003055197,0.00001657669,0.0007417751,0.2881545,0.0008874482,0.6528081,0.01462114,0.03090364],"study_design_scores_gemma":[0.0002123004,0.00007208857,0.02111909,0.0000454233,0.0002317763,0.000004123511,0.00002699307,0.9538018,0.00003010192,0.002357851,0.02195371,0.0001447664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1414349,0.0001982094,0.8533198,0.001604702,0.0008508656,0.000222379,0.000005267244,0.0004764541,0.001887375],"genre_scores_gemma":[0.9657015,0.000006444409,0.0256353,0.0002174464,0.0002842527,0.00002808095,0.00002094452,0.00001078359,0.008095236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8276845,"threshold_uncertainty_score":0.4792843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01858993400552715,"score_gpt":0.235784293668071,"score_spread":0.2171943596625438,"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."}}