{"id":"W2951507749","doi":"10.48550/arxiv.1904.04003","title":"Application Component Placement in NFV-based Hybrid Cloud/Fog Systems with Mobile Fog Nodes","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Waypoint; Cloud computing; Job shop scheduling; Distributed computing; Integer programming; Component (thermodynamics); Tabu search; Enhanced Data Rates for GSM Evolution; Latency (audio); Mathematical optimization; Algorithm; Real-time computing; Computer network; Routing (electronic design automation); Mathematics; 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":[0.0004822625,0.0005954409,0.0005892543,0.0004119402,0.0007979514,0.000907267,0.001045035,0.0006060587,0.0009039656],"category_scores_gemma":[0.0007623481,0.0003562244,0.0004159868,0.0006452522,0.0004960352,0.0007889862,0.0006478843,0.0002903607,0.0001154475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001293568,"about_ca_system_score_gemma":0.001019403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01244627,"about_ca_topic_score_gemma":0.01346652,"domain_scores_codex":[0.9996548,0.00008923494,0.0000125312,0.00008076325,0.0000709012,0.00009185821],"domain_scores_gemma":[0.9997582,0.00009353529,0.00003800582,0.00003287502,0.00003933987,0.00003807515],"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":[0.0001564179,0.0000554514,0.0005623578,0.00005059343,0.00003483705,0.0001344228,0.00005781099,0.9591779,0.00645892,0.003188985,0.0007534522,0.02936897],"study_design_scores_gemma":[0.000005945135,0.00003289636,0.0002398161,0.000003355022,0.000006960796,0.0000301069,0.00002566667,0.9964557,0.001052285,0.001730471,0.0004125426,0.00000436966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2326065,0.0006257338,0.7614622,0.0001687981,0.00008167332,0.0001102927,0.0001121241,0.0005860933,0.004246646],"genre_scores_gemma":[0.9123823,0.0001414023,0.08581277,0.00004355279,0.00001105752,0.00004896044,0.00006868695,0.00005168432,0.001439487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01244627,"threshold_uncertainty_score":0.02474767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.030804409656117,"score_gpt":0.1700453441733991,"score_spread":0.1392409345172821,"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."}}