{"id":"W4297818342","doi":"10.1109/dcoss54816.2022.00066","title":"GNN-based End-to-end Delay Prediction in Software Defined Networking","year":2022,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; End-to-end delay; End-to-end principle; Network delay; Latency (audio); Network packet; Software; Graph; Real-time computing; Computer network; Convolutional neural network; Recurrent neural network; Software-defined networking; Artificial neural network; Artificial intelligence; Theoretical computer science; Telecommunications","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":[0.0006137993,0.0001913047,0.0002050167,0.000285577,0.0003527779,0.0001246885,0.0008406292,0.00005906696,0.0005040456],"category_scores_gemma":[0.00004959508,0.0001953981,0.00008550876,0.001679638,0.00001750751,0.0002004712,0.0005598679,0.0003387507,0.00004537353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002072443,"about_ca_system_score_gemma":0.0001486845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002278605,"about_ca_topic_score_gemma":0.0001819839,"domain_scores_codex":[0.997822,0.0001477975,0.0003546913,0.0006088594,0.0005190775,0.0005475612],"domain_scores_gemma":[0.9987451,0.0004030631,0.00007714215,0.0005892036,0.00003962752,0.0001458961],"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":[0.00005828877,0.0001932635,0.107278,0.00001055781,0.00001849446,0.00009536948,0.0004219165,0.6450791,0.00002666446,0.005555314,0.03164308,0.2096199],"study_design_scores_gemma":[0.001355316,0.0004824565,0.02580059,0.00004195829,0.00001120789,0.00004260464,0.0000418583,0.866871,0.00007141622,0.002693063,0.102026,0.0005624641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01646245,0.0002467122,0.9780474,0.001103793,0.001719304,0.0003186913,0.00001144326,0.0009119811,0.001178275],"genre_scores_gemma":[0.9160491,0.000003941341,0.07901239,0.004019581,0.0002327374,0.0002106532,0.00003482917,0.0000257966,0.0004109458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8995867,"threshold_uncertainty_score":0.7968103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01571406255340447,"score_gpt":0.2184738993802319,"score_spread":0.2027598368268274,"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."}}