{"id":"W2886086578","doi":"","title":"Siphon: expediting inter-datacenter coflows in wide-area data analytics","year":2018,"lang":"en","type":"article","venue":"USENIX Annual Technical Conference","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Expediting; Analytics; Cloud computing; Distributed computing; Scheduling (production processes); Siphon (mollusc); SPARK (programming language); Microservices; Database; Operating system; Engineering; Systems 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":[0.001422054,0.0008115213,0.0005043535,0.0005909458,0.0005920513,0.0007152553,0.001300129,0.0004350262,0.001352553],"category_scores_gemma":[0.002012558,0.0003192909,0.0004077214,0.000367065,0.0006525627,0.001078931,0.001577007,0.0008380437,0.0003173569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006219053,"about_ca_system_score_gemma":0.001473361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002420825,"about_ca_topic_score_gemma":0.002812377,"domain_scores_codex":[0.9994331,0.000140408,0.00002810965,0.0001154885,0.0001681389,0.0001147568],"domain_scores_gemma":[0.9990675,0.0003338069,0.00007502833,0.0001961144,0.0001155497,0.000212059],"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":[0.005139543,0.001819503,0.01218439,0.0005193581,0.0003293906,0.0009364288,0.000849226,0.2958519,0.2316999,0.01904444,0.05197215,0.3796538],"study_design_scores_gemma":[0.0001756077,0.0007184354,0.002273679,0.00001877747,0.00004060902,0.0001466759,0.00008890523,0.9364511,0.04712252,0.003913838,0.008992449,0.00005748042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5014384,0.001311345,0.4365569,0.0005237025,0.0007379359,0.0005286965,0.0005626862,0.05019199,0.008148246],"genre_scores_gemma":[0.861187,0.000256308,0.1339194,0.0002628387,0.00009958817,0.000180279,0.0008351722,0.0008186968,0.002440638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002420825,"threshold_uncertainty_score":0.007520616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08113551594113265,"score_gpt":0.3045707446843762,"score_spread":0.2234352287432435,"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."}}