{"id":"W2896208823","doi":"10.1109/tcc.2018.2876242","title":"Cloud Resource Scaling for Time-Bounded and Unbounded Big Data Streaming Applications","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Scaling; Big data; Resource (disambiguation); Bounded function; Distributed computing; Latency (audio); Data mining; Computer network; Mathematics","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001078607,0.0003503326,0.0003399008,0.0002652129,0.002127356,0.0005795639,0.00184883,0.0001273099,0.000003877745],"category_scores_gemma":[0.00001938495,0.0003627566,0.0001142113,0.0007903068,0.0002370998,0.00005774431,0.0001287606,0.0003258778,0.00005476638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009956166,"about_ca_system_score_gemma":0.00007319912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004336156,"about_ca_topic_score_gemma":0.00002067145,"domain_scores_codex":[0.9969464,0.0001316865,0.0005533712,0.001332863,0.0003839796,0.0006517026],"domain_scores_gemma":[0.9966727,0.0008217961,0.0002256189,0.001938446,0.0001445008,0.0001968667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004527504,0.000253367,0.000006489512,0.00008410438,0.0001587412,0.000003125584,0.001140627,0.01786152,0.0003232614,0.002662244,0.001094671,0.9763666],"study_design_scores_gemma":[0.0008725707,0.00023463,0.00001997425,0.0001450411,0.00006664814,0.00003107576,0.0001659593,0.9478463,0.001644521,0.001228236,0.04728672,0.0004583457],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04535952,0.00007177624,0.9501417,0.0008565881,0.00133725,0.0007409042,0.00002401376,0.0007228336,0.0007454109],"genre_scores_gemma":[0.937135,0.000003594145,0.05897045,0.0006549645,0.002485998,0.00003367321,0.000009322364,0.00005304274,0.0006539568],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9759082,"threshold_uncertainty_score":0.9998825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120164471267636,"score_gpt":0.2754707757006436,"score_spread":0.2342691309879673,"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."}}