{"id":"W4408010932","doi":"10.18280/isi.300225","title":"Optimizing Microservices Performance and Scalability Through Automated Monitoring with Kubernetes and Prometheus","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Microservices; Scalability; Computer science; Software engineering; Operating system; Cloud computing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000533585,0.0001951438,0.0002379438,0.0001435736,0.0004432781,0.0005606319,0.0002813028,0.0001038615,7.920372e-7],"category_scores_gemma":[0.00006161656,0.0001480617,0.00002079097,0.0006270768,0.0001988892,0.006555836,0.0002291952,0.0001239626,0.000004770181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001235641,"about_ca_system_score_gemma":0.00008599654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001120889,"about_ca_topic_score_gemma":0.000003376237,"domain_scores_codex":[0.9988073,0.00004889095,0.0004431681,0.0002399793,0.0001853704,0.0002753093],"domain_scores_gemma":[0.9990502,0.00010074,0.0001838098,0.0003673683,0.0002480322,0.00004987374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008261154,0.00004361806,0.7351515,0.006633202,0.0001040769,0.000001184606,0.03847864,0.00141345,0.0003850326,0.001453425,0.00004540225,0.2162078],"study_design_scores_gemma":[0.001137967,0.0002992213,0.6889785,0.002050722,0.00003135543,0.00008131787,0.001637349,0.2892265,0.01371717,0.001421442,0.0008684776,0.0005500381],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709189,0.0004592956,0.02589138,0.00006911749,0.0002422727,0.0004054235,0.000001924217,0.0007339233,0.001277764],"genre_scores_gemma":[0.9492787,0.0001191781,0.05044915,0.00005243123,0.00001625402,0.00006102327,0.000003356372,0.00000436015,0.00001561071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.287813,"threshold_uncertainty_score":0.6037781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008523873238325574,"score_gpt":0.2346934320659213,"score_spread":0.2261695588275957,"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."}}