{"id":"W2096253463","doi":"10.1002/cpe.2805","title":"Automatic configuration generation for service high availability with load balancing","year":2012,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; High availability; Redundancy (engineering); Workload; Distributed computing; Task (project management); Service (business); Load balancing (electrical power); IT service continuity; Computer network; Operating system; Systems engineering; Engineering","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":[0.0003907162,0.0001596739,0.0001462693,0.00004284943,0.0003613724,0.0002493766,0.000149486,0.00005109717,0.000006983109],"category_scores_gemma":[0.00006758785,0.0001331471,0.00001341382,0.000284897,0.00004231231,0.003096003,0.00006124825,0.00008909302,0.000005117191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002319022,"about_ca_system_score_gemma":0.0000870552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001703262,"about_ca_topic_score_gemma":0.00004673156,"domain_scores_codex":[0.998798,0.0001040354,0.0002486065,0.0003713119,0.0002372856,0.0002407786],"domain_scores_gemma":[0.9986224,0.0004420528,0.0002094997,0.0001892024,0.0004103085,0.0001265502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001386002,0.0004244686,0.004851779,0.0007457312,0.00008460687,0.000003838612,0.2176699,0.0005404007,0.0104925,0.1371287,0.0002064848,0.6277129],"study_design_scores_gemma":[0.002018573,0.0005656945,0.01318768,0.0001389895,0.0001005264,0.0002183315,0.008634365,0.9539002,0.006615561,0.001844966,0.01197612,0.0007990317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5330006,0.0006851643,0.4642098,0.0011769,0.0003066422,0.0002782023,0.000002339747,0.00008275861,0.0002575957],"genre_scores_gemma":[0.9629279,0.00003208508,0.03440288,0.002378439,0.0001303994,0.00009640343,0.00002134668,0.000005685036,0.00000479666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9533598,"threshold_uncertainty_score":0.5429582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02179267942427731,"score_gpt":0.2919101270302058,"score_spread":0.2701174476059285,"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."}}