{"id":"W2965979319","doi":"10.1109/sds.2019.8768654","title":"Optimized Availability-Aware Component Scheduler for Applications in Container-Based Cloud","year":2019,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Cloud computing; Computer science; Distributed computing; Scheduling (production processes); Integer programming; Schedule; Data center; Component (thermodynamics); High availability; Container (type theory); Computer network; Operating system; Mathematical optimization; Algorithm; Engineering","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":[0.0004130664,0.0005382798,0.0004945971,0.0003183308,0.000479637,0.0007069612,0.0006988115,0.0002414265,0.0009944073],"category_scores_gemma":[0.0008021145,0.0002205344,0.0002613567,0.0004133071,0.000205381,0.0003623556,0.0002851966,0.0004050688,0.0001599899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008281273,"about_ca_system_score_gemma":0.002185499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009392234,"about_ca_topic_score_gemma":0.01214968,"domain_scores_codex":[0.9997149,0.00006176592,0.00001387929,0.00005224956,0.00008165833,0.00007563736],"domain_scores_gemma":[0.9996613,0.0001090885,0.00004858763,0.00002881419,0.0001007091,0.00005143621],"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":[0.0003612601,0.0001138848,0.001590489,0.0001238137,0.00004017391,0.0001565466,0.00005808327,0.9283452,0.0266379,0.004101361,0.0025871,0.03588411],"study_design_scores_gemma":[0.00001458349,0.00003863502,0.0003227041,0.000001802574,0.00001008458,0.00001492046,0.00001295835,0.9968292,0.001870168,0.0004416574,0.0004398931,0.000003471278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3796202,0.001687453,0.6063569,0.0004854202,0.0002765013,0.0002241346,0.000251848,0.001999757,0.009097902],"genre_scores_gemma":[0.9510116,0.0002069792,0.04746374,0.00003815266,0.00001960261,0.00004340159,0.0001011088,0.00006819725,0.00104718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009392234,"threshold_uncertainty_score":0.01867515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367195869866808,"score_gpt":0.2446029439057522,"score_spread":0.2309309852070841,"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."}}