{"id":"W4402569568","doi":"10.1109/tnsm.2024.3462831","title":"Adaptive Feature Selection for Predicting Application Performance Degradation in Edge Cloud Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Computer science; Cloud computing; Degradation (telecommunications); Feature selection; Selection (genetic algorithm); Enhanced Data Rates for GSM Evolution; Feature (linguistics); Artificial intelligence; Telecommunications; 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":[0.0003218776,0.0001649307,0.0001101866,0.0001650133,0.0003022309,0.0001336237,0.0002158895,0.00006195038,0.000001165596],"category_scores_gemma":[1.906097e-7,0.0001619486,0.00004307989,0.0007170677,0.000009844655,0.00007965935,0.0000125203,0.000185505,0.00001091932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226689,"about_ca_system_score_gemma":0.00000752007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002363581,"about_ca_topic_score_gemma":0.00008479605,"domain_scores_codex":[0.9988053,0.00003900919,0.0001845524,0.0005137362,0.0001838173,0.0002735489],"domain_scores_gemma":[0.9996298,0.0000615122,0.00004243989,0.0002099711,0.00001222715,0.00004407636],"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":[0.00002379353,0.00005082402,0.00004362845,0.0001533554,0.00005155344,9.452955e-7,0.0002655758,0.5611048,0.00000790425,0.00130541,0.0002131598,0.4367791],"study_design_scores_gemma":[0.0003041909,0.0001187861,0.000925221,0.0001759176,0.00004201966,0.000002817472,0.00009073174,0.9824862,0.00008413932,0.0002176719,0.01539578,0.0001565499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02957306,0.0001067509,0.9668474,0.001158645,0.0006589375,0.0008006559,0.000001474617,0.0001950906,0.0006579798],"genre_scores_gemma":[0.9870686,0.000192427,0.01075811,0.0004178143,0.0001989418,0.0003296213,0.000004428436,0.00001758046,0.001012468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9574956,"threshold_uncertainty_score":0.6604072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009337657165588544,"score_gpt":0.2065210627465352,"score_spread":0.1971834055809467,"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."}}