{"id":"W1991080284","doi":"10.1109/glocom.2014.7036971","title":"Resource pooling in network virtualization and heterogeneous scenarios using Stochastic Petri nets","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Pooling; Computer science; Provisioning; Stochastic Petri net; Virtualization; Distributed computing; Resource (disambiguation); Resource allocation; Petri net; Blocking (statistics); Wireless network; Markov process; Heterogeneous network; Computer network; Wireless; Artificial intelligence","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":[0.00209535,0.001326162,0.0009698652,0.000780411,0.0006348497,0.001851165,0.0009876884,0.0008446717,0.001571313],"category_scores_gemma":[0.002911713,0.0006195342,0.001315925,0.0007447951,0.001414804,0.001489043,0.001433372,0.000947536,0.0001284953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002206407,"about_ca_system_score_gemma":0.001513066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01506409,"about_ca_topic_score_gemma":0.009394347,"domain_scores_codex":[0.9987256,0.000633746,0.00005455602,0.0001451224,0.0001844077,0.0002565787],"domain_scores_gemma":[0.9979402,0.001446982,0.0002640081,0.00007123853,0.0001368199,0.0001407724],"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":[0.0000238811,0.000009592229,0.0002224806,0.00001288892,0.00001365581,0.00005322503,0.000012472,0.9849122,0.0002935475,0.01334389,0.00006131201,0.001041004],"study_design_scores_gemma":[0.000002649289,0.00000749893,0.00004305072,0.000002267185,0.000004671715,0.000005021779,0.00000574227,0.9952237,0.00008176311,0.00453688,0.00008411297,0.000002663806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07998491,0.000495512,0.9125287,0.0002863592,0.00007087584,0.00008227404,0.0001991694,0.0002512345,0.006100959],"genre_scores_gemma":[0.9679808,0.0004341284,0.02854066,0.00005238034,0.00003487965,0.0001166125,0.0001217814,0.00003899576,0.00267981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01506409,"threshold_uncertainty_score":0.02995282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007020836164224374,"score_gpt":0.2006085776740508,"score_spread":0.1935877415098264,"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."}}