{"id":"W4311079977","doi":"10.1145/3570611","title":"Malcolm: Multi-agent Learning for Cooperative Load Management at Rack Scale","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scalability; Server; Load balancing (electrical power); Distributed computing; Latency (audio); Scheduling (production processes); Nash equilibrium; Price of anarchy; Load management; Throughput; Mathematical optimization; Computer network; Operating system; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003797847,0.0002267587,0.0005736867,0.0003649779,0.0009697417,0.0001217275,0.002164427,0.00002756872,0.00000191168],"category_scores_gemma":[0.0001731627,0.0001746593,0.0003576253,0.001333549,0.00005474103,0.00001831332,0.00395604,0.0001635848,6.728333e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003815685,"about_ca_system_score_gemma":0.00001893446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002824818,"about_ca_topic_score_gemma":0.000002331476,"domain_scores_codex":[0.9968061,0.00008940549,0.0006493722,0.0005829534,0.001558686,0.0003135066],"domain_scores_gemma":[0.9978482,0.0001012606,0.0008653841,0.0005139336,0.0006099715,0.00006130586],"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":[0.00007850418,0.0004194737,0.02946984,0.0007146106,0.003512582,7.318511e-7,0.004817699,0.9460714,0.004139723,0.003784311,0.001873597,0.00511748],"study_design_scores_gemma":[0.001028369,0.0002753035,0.009891488,0.0002445946,0.00083215,0.000002115307,0.002074643,0.9816942,0.001648263,0.00003572448,0.002009976,0.0002632085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852977,0.0004417261,0.01136001,0.000682948,0.000332341,0.0009751958,0.000002807816,0.00008580794,0.000821501],"genre_scores_gemma":[0.99547,0.000004338407,0.003248503,0.00006404362,0.00003431562,0.00004553341,9.441583e-7,0.00001275795,0.001119555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03562272,"threshold_uncertainty_score":0.7458568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03638996529677906,"score_gpt":0.2476713601500177,"score_spread":0.2112813948532386,"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."}}