{"id":"W1978252943","doi":"10.1109/tpds.2015.2425403","title":"Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"State Key Laboratory of Novel Software Technology; Nanjing University; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Burstiness; Computer science; Reservation; Distributed computing; Queue; Cloud computing; Virtualization; Virtual machine; Server; Energy consumption; Computer network; Real-time computing; Operating system","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":[0.0008693322,0.0004099962,0.0005948552,0.0003475134,0.000505228,0.0008647129,0.001211065,0.0003916076,0.0005779124],"category_scores_gemma":[0.002145263,0.0002569623,0.0003384391,0.0004998987,0.0004141818,0.001190976,0.0005483305,0.0007107729,0.0001366136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009405754,"about_ca_system_score_gemma":0.001393624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004319023,"about_ca_topic_score_gemma":0.003382333,"domain_scores_codex":[0.9994358,0.0001437179,0.00004677859,0.0001143612,0.0001341657,0.0001250603],"domain_scores_gemma":[0.9989793,0.00044393,0.0001365296,0.0001058511,0.0002105282,0.0001238809],"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":[0.0003592692,0.0002357995,0.004787899,0.0001511992,0.00008125242,0.0001699088,0.0002427663,0.7986636,0.03549839,0.01908988,0.002355423,0.1383646],"study_design_scores_gemma":[0.000006892444,0.00002732665,0.0001730177,0.000002872795,0.000005951418,0.0000202718,0.00001159278,0.9963564,0.001415076,0.001721931,0.0002539846,0.000004751523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08481672,0.00120344,0.9118922,0.0002542104,0.0001055051,0.00006532032,0.00003373277,0.0006161723,0.001012713],"genre_scores_gemma":[0.9566714,0.0003223194,0.04227582,0.00008685423,0.00003866252,0.00002623797,0.00004190754,0.00004185638,0.0004948107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004319023,"threshold_uncertainty_score":0.008587718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04798110418448325,"score_gpt":0.2702338779129049,"score_spread":0.2222527737284216,"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."}}