{"id":"W2512459603","doi":"10.1109/tpds.2016.2605684","title":"Semi-Online Algorithms for Computational Task Offloading with Communication Delay","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristics; Scheduling (production processes); A priori and a posteriori; Job shop scheduling; Cloud computing; Distributed computing; Task (project management); Set (abstract data type); Parallel computing; Algorithm; Real-time computing; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.0002315155,0.0001672264,0.0001928419,0.00008801447,0.00045069,0.0001256993,0.0003328313,0.00006047427,0.000001270236],"category_scores_gemma":[0.000002891449,0.0001084421,0.0000595022,0.0002063353,0.00006297084,0.00004437027,0.000007177834,0.00008400308,0.000006339503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006535504,"about_ca_system_score_gemma":0.00002798736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004590028,"about_ca_topic_score_gemma":0.00001049181,"domain_scores_codex":[0.9988111,0.00008164249,0.0002798385,0.0003469858,0.0002224743,0.0002579118],"domain_scores_gemma":[0.9989384,0.0003349041,0.0001162129,0.0003797315,0.0001248428,0.0001058933],"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":[0.00003044954,0.0001427063,0.00002766825,0.00002977968,0.00008456898,0.000002628169,0.0001010762,0.9805977,0.00003276162,0.001367508,0.0003945013,0.01718863],"study_design_scores_gemma":[0.001544406,0.0002047449,0.0001762989,0.0002032427,0.00002702266,0.00005658939,0.0001151132,0.9931186,0.00001655192,0.0002686858,0.004047238,0.0002215465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01622021,0.0001447612,0.9812937,0.001251112,0.000187503,0.0003892659,0.0002808527,0.0001975586,0.00003498245],"genre_scores_gemma":[0.9867032,0.00001347583,0.01270086,0.00005875555,0.00003476575,0.00009086091,0.00003099425,0.00001062802,0.0003564152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.970483,"threshold_uncertainty_score":0.4422141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139338181619072,"score_gpt":0.2470284443475481,"score_spread":0.2256350625313574,"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."}}