{"id":"W2064618181","doi":"10.1109/noms.2014.6838351","title":"Traffic engineering in cloud data centers: A column generation approach","year":2014,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Column generation; Computer science; Scalability; Virtual LAN; Cloud computing; Distributed computing; Traffic engineering; Integer programming; Data center; Mathematical optimization; Linear programming; Computer network; Algorithm; Mathematics","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":[0.0008554342,0.0007287866,0.0008658833,0.0007527497,0.0006025493,0.001252609,0.0009111536,0.0007233239,0.002710766],"category_scores_gemma":[0.001773208,0.0004769028,0.0005857027,0.00112345,0.0007371605,0.001047169,0.0008000485,0.00107744,0.0002681812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001209502,"about_ca_system_score_gemma":0.001286435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004965123,"about_ca_topic_score_gemma":0.00583959,"domain_scores_codex":[0.9995578,0.000206251,0.00001086478,0.00005334309,0.00009430017,0.00007754954],"domain_scores_gemma":[0.9988587,0.0006796971,0.0001106183,0.00009221805,0.0001790519,0.00007973184],"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":[0.00003528961,0.00005178843,0.0002777684,0.0000421988,0.00001510524,0.00005367016,0.00002845295,0.9640355,0.001154764,0.01359871,0.001515404,0.01919132],"study_design_scores_gemma":[0.000003688729,0.000009473398,0.0000247046,0.000002435695,0.000002486565,0.000009254122,0.000008749386,0.9947642,0.0002422214,0.004643927,0.0002865482,0.000002338076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02609996,0.0003261188,0.9691547,0.0004536329,0.00005272549,0.0001181683,0.0001566533,0.0002051814,0.003432786],"genre_scores_gemma":[0.5750844,0.0006366128,0.4189037,0.0003331932,0.0001281496,0.0002664554,0.0004587782,0.0001833382,0.004005408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004965123,"threshold_uncertainty_score":0.009872437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03062915596460568,"score_gpt":0.2173404343165637,"score_spread":0.186711278351958,"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."}}