{"id":"W2920437306","doi":"10.1109/tcc.2019.2901669","title":"Elephant Flow Detection and Load-Balanced Routing with Efficient Sampling and Classification","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Huawei Technologies; National Natural Science Foundation of China","keywords":"Computer science; Resource consumption; Routing (electronic design automation); Overhead (engineering); Network packet; Distributed computing; Real-time computing; Computer network","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":[0.001301189,0.0007349821,0.0008482764,0.001587018,0.0007142846,0.000838893,0.001364453,0.0005989824,0.0005407489],"category_scores_gemma":[0.003877041,0.0003089657,0.0004971261,0.0009847676,0.0006147464,0.001665222,0.001019327,0.0007464941,0.0001874977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007692251,"about_ca_system_score_gemma":0.0009250591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001451528,"about_ca_topic_score_gemma":0.001810808,"domain_scores_codex":[0.9985269,0.0002968378,0.0001034392,0.0004428572,0.0004657338,0.0001641788],"domain_scores_gemma":[0.9977697,0.0007796279,0.0004328784,0.000422998,0.0004826767,0.000112178],"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":[0.000833226,0.000576237,0.01967644,0.0001319165,0.0001361018,0.0003013539,0.0002870104,0.278648,0.04630282,0.01428976,0.002847084,0.6359701],"study_design_scores_gemma":[0.00001171136,0.00005434411,0.0009348722,0.00000426097,0.00001201011,0.00008290573,0.00002154799,0.9898909,0.005342202,0.003163463,0.0004718615,0.00000986601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04770064,0.0001167212,0.9506706,0.0000936905,0.00002888598,0.0000787633,0.0000382596,0.0006747589,0.0005976174],"genre_scores_gemma":[0.7533677,0.0001296383,0.2448959,0.0001415435,0.00007736804,0.0001426817,0.0002344631,0.00005714127,0.000953484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001587018,"threshold_uncertainty_score":0.006881416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686979084311534,"score_gpt":0.230529234477891,"score_spread":0.2136594436347757,"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."}}