{"id":"W4312517346","doi":"10.1109/tnet.2022.3213426","title":"Fair Multi-Resource Allocation in Heterogeneous Servers With an External Resource Type","year":2022,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Server; Resource allocation; Computer science; Resource management (computing); Mobile edge computing; Shared resource; Max-min fairness; Resource (disambiguation); Computer network; Upload; Distributed computing; Game theory; Microeconomics; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004558637,0.0002291925,0.0001866965,0.0002920396,0.0007262911,0.0001479197,0.001289492,0.0000422078,0.00001227751],"category_scores_gemma":[0.000002098269,0.0002310437,0.00007734003,0.001070065,0.00003382283,0.00005134413,0.00006530804,0.000527908,0.000007247383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000247615,"about_ca_system_score_gemma":0.0000373753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001409685,"about_ca_topic_score_gemma":0.0001894542,"domain_scores_codex":[0.9977744,0.0003311873,0.0002582395,0.0006657196,0.0005131447,0.0004572857],"domain_scores_gemma":[0.9986476,0.0001060625,0.0001184501,0.0009867303,0.0000268498,0.0001142938],"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":[0.00007644038,0.0002633856,0.0001468712,0.000006175015,0.00002275978,0.00006882077,0.001625189,0.8954293,0.00003781234,0.00002121251,0.00002202648,0.1022801],"study_design_scores_gemma":[0.0008197766,0.0006615413,0.0002926952,0.00007406703,0.00001761667,0.00009547505,0.0003659231,0.9813353,0.0001518134,0.00007334963,0.01577561,0.0003368239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4410683,0.00009160521,0.5572201,0.0003610536,0.000509317,0.0002655511,0.000001139937,0.0003228964,0.0001600623],"genre_scores_gemma":[0.9891204,0.000002967252,0.009666587,0.0005338945,0.0001637703,0.00004874069,0.000002798696,0.00003446978,0.0004264351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5480521,"threshold_uncertainty_score":0.9421687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02781571510947511,"score_gpt":0.2447873800142804,"score_spread":0.2169716649048053,"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."}}