{"id":"W4315606013","doi":"10.1109/globecom48099.2022.10001247","title":"Scheduler Design for Mobility-aware Multipath QUIC","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Goodput; Multihoming; Computer science; Computer network; Mobility model; Multipath propagation; Network packet; Multipath TCP; Scheduling (production processes); Mobility management; Distributed computing; Wireless; The Internet; Internet Protocol; Telecommunications; Throughput; Channel (broadcasting)","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":[0.001144079,0.0004967,0.000554379,0.0005723739,0.0007479496,0.0009814585,0.001493973,0.0003287256,0.001906942],"category_scores_gemma":[0.002588447,0.0003002841,0.000214446,0.0005999188,0.0003084798,0.0005898682,0.0008215256,0.0006564285,0.0005257108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001335619,"about_ca_system_score_gemma":0.004050781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006049687,"about_ca_topic_score_gemma":0.00836732,"domain_scores_codex":[0.9994991,0.0001009546,0.00004510528,0.00007877905,0.0001799031,0.0000961216],"domain_scores_gemma":[0.9986999,0.0002996698,0.0001467456,0.0001320231,0.0005697968,0.0001517978],"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":[0.0006219209,0.0002107347,0.005141984,0.0003276566,0.000118662,0.0004867365,0.0003044818,0.6469052,0.05950969,0.04362714,0.0111428,0.2316029],"study_design_scores_gemma":[0.00004471904,0.0000935753,0.0002876138,0.000007533065,0.00002226164,0.00007609367,0.00002743885,0.9853972,0.006874156,0.001835571,0.005316582,0.00001740962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03768417,0.0008528188,0.9545532,0.0002206495,0.0002615155,0.000306008,0.0001703344,0.002480041,0.003471322],"genre_scores_gemma":[0.7307906,0.0005020911,0.2647566,0.0001561385,0.0001402412,0.0002516899,0.0002428094,0.0002103582,0.002949468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006049687,"threshold_uncertainty_score":0.01202893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09581041099166503,"score_gpt":0.3340087284376369,"score_spread":0.2381983174459719,"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."}}