{"id":"W2892022253","doi":"10.1109/cloud.2018.00148","title":"Deploying Microservice Based Applications with Kubernetes: Experiments and Lessons Learned","year":2018,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microservices; Maintainability; Computer science; Downtime; High availability; Flexibility (engineering); Upgrade; Distributed computing; Architectural style; Code refactoring; Service (business); Set (abstract data type); Survivability; Software engineering; Operating system; Architecture; Software; Cloud computing; Computer network; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001479279,0.0000973391,0.00009520887,0.00003996442,0.0002329756,0.0001120731,0.0003426045,0.00003983781,0.00001503708],"category_scores_gemma":[0.000004005984,0.00006778505,0.00001532924,0.0002634377,0.00009325908,0.0002647218,0.0001069376,0.00004829465,0.00008638057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001969254,"about_ca_system_score_gemma":0.00005646518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009857188,"about_ca_topic_score_gemma":0.00003816286,"domain_scores_codex":[0.9991835,0.00002338329,0.0001185348,0.000362533,0.0001303255,0.0001817246],"domain_scores_gemma":[0.9991854,0.00004890342,0.00004243907,0.0005422342,0.000105607,0.00007544806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001696067,0.001144758,0.4236206,0.0007918209,0.0002169717,0.00001026748,0.01321112,0.0001838181,0.05738385,0.04174121,0.002718872,0.4588071],"study_design_scores_gemma":[0.006633572,0.001671202,0.09491523,0.0004450231,0.00006343185,0.000107302,0.001497523,0.2765703,0.4577541,0.003855475,0.1539837,0.00250324],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.160853,0.0001039502,0.8344231,0.001222937,0.00006005333,0.0003392475,8.737459e-7,0.0004075216,0.00258922],"genre_scores_gemma":[0.9257097,0.000003954448,0.07333054,0.0006399547,0.00004295494,0.00009686039,9.298666e-7,0.000007196734,0.0001679131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7648566,"threshold_uncertainty_score":0.2764194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03415740404709071,"score_gpt":0.3078189240316829,"score_spread":0.2736615199845921,"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."}}