{"id":"W4220812597","doi":"10.23919/icact53585.2022.9728923","title":"TCP Congestion Avoidance in Data Centres using Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"2022 24th International Conference on Advanced Communication Technology (ICACT)","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"TCP tuning; Computer science; Computer network; Zeta-TCP; TCP Friendly Rate Control; Reinforcement learning; TCP acceleration; Network congestion; Throughput; The Internet; Transmission Control Protocol; TCP Westwood plus; H-TCP; TCP global synchronization; Artificial intelligence; Network packet; Telecommunications; Wireless; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001899531,0.0005522065,0.0009729934,0.0004633554,0.0006165575,0.0009940218,0.001491693,0.0007365051,0.0005821383],"category_scores_gemma":[0.003847954,0.0002752072,0.0004413124,0.0003411566,0.001102175,0.0007278475,0.001398737,0.001155005,0.0001020384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259692,"about_ca_system_score_gemma":0.001277421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007727395,"about_ca_topic_score_gemma":0.003963653,"domain_scores_codex":[0.999148,0.0003320214,0.00003808124,0.0001635085,0.0001664734,0.0001518004],"domain_scores_gemma":[0.9974501,0.001307708,0.0003175378,0.0001697402,0.0005118809,0.0002429951],"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":[0.0001220988,0.0001103944,0.001261351,0.00003562039,0.00003601193,0.00007416716,0.00007682413,0.9643666,0.003412591,0.00340192,0.0004348805,0.02666761],"study_design_scores_gemma":[0.000005229591,0.00001913989,0.00006173913,0.000001484696,0.000002911272,0.000004234616,0.000003797507,0.9989355,0.0003282625,0.000579808,0.00005554533,0.000002336905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1094765,0.0003166348,0.8869363,0.0002510275,0.00005491713,0.0001136077,0.00001464763,0.0006464581,0.002189843],"genre_scores_gemma":[0.9777721,0.00005492053,0.02154685,0.00004311843,0.00001500759,0.00004838171,0.00001044867,0.00001494344,0.0004941702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007727395,"threshold_uncertainty_score":0.01536483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05242392085753073,"score_gpt":0.3123617156049461,"score_spread":0.2599377947474154,"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."}}