{"id":"W2528870978","doi":"10.1002/spe.2441","title":"Toward cost‐effective replica placements in cloud storage systems with QoS‐awareness","year":2016,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Crandall University; University of New Brunswick","funders":"Science and Technology Planning Project of Guangdong Province; Korea Institute for Advancement of Technology; National Key Research and Development Program of China; National University of Singapore","keywords":"Replica; Computer science; Quality of service; Distributed computing; Cloud computing; Greedy algorithm; Cloud storage; Workflow; Set (abstract data type); Replication (statistics); Computer network; Database; Algorithm; Operating system; 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":[0.001241259,0.0004865454,0.0006787867,0.0004160824,0.0005914081,0.001187741,0.001175128,0.000726111,0.001080736],"category_scores_gemma":[0.003871682,0.0004586682,0.0004117743,0.0005587438,0.000738024,0.001084056,0.000867306,0.0006259457,0.0001824887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001468829,"about_ca_system_score_gemma":0.001726658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007628083,"about_ca_topic_score_gemma":0.004025629,"domain_scores_codex":[0.9992833,0.0003341395,0.00002941169,0.00006990545,0.0001950127,0.00008832976],"domain_scores_gemma":[0.9986337,0.0007344623,0.0001537073,0.0001880536,0.0002163986,0.00007363103],"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":[0.00003069088,0.0000115944,0.0002228226,0.00001460967,0.000006103211,0.00002191946,0.00001586482,0.988629,0.001602197,0.006187103,0.000168659,0.003089414],"study_design_scores_gemma":[0.000003913978,0.000005010706,0.00002307881,0.000001125988,0.000001382932,0.000005043349,0.000005773627,0.9978661,0.0003333838,0.001616919,0.000136788,0.000001433646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1530991,0.0004819364,0.840648,0.0007881765,0.00005948739,0.00009065314,0.00007232324,0.0005142859,0.004245939],"genre_scores_gemma":[0.9162832,0.0002192892,0.08249516,0.00004619627,0.00001335448,0.00005833349,0.00003597045,0.00004083655,0.0008076675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007628083,"threshold_uncertainty_score":0.01516736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218812459417729,"score_gpt":0.2893670511900401,"score_spread":0.2674858052482673,"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."}}