{"id":"W4382199008","doi":"10.1145/3606376.3593550","title":"Malcolm: Multi-agent Learning for Cooperative Load Management at Rack Scale","year":2023,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Server; Scalability; Distributed computing; Load balancing (electrical power); Latency (audio); Nash equilibrium; Scheduling (production processes); Load management; Mathematical optimization; Computer network; Operating system; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007112168,0.0002600938,0.0003657114,0.0004431904,0.000684578,0.0001365622,0.001329176,0.00005401553,0.00006585775],"category_scores_gemma":[0.001153585,0.0002281153,0.0001719358,0.004540091,0.00003000143,0.00009966861,0.001620153,0.0001661695,0.001292155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005802261,"about_ca_system_score_gemma":0.00007912248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001998462,"about_ca_topic_score_gemma":0.000001632651,"domain_scores_codex":[0.9965078,0.0002619869,0.0006020479,0.0007210075,0.00140229,0.0005049282],"domain_scores_gemma":[0.9974872,0.0003428797,0.0003094104,0.0010707,0.0006810247,0.000108798],"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":[0.00000526804,0.00006729821,0.00120856,0.001896808,0.00006731105,0.000002755668,0.0002841032,0.09537503,0.000008465761,0.0001193369,0.01904018,0.8819249],"study_design_scores_gemma":[0.0009055259,0.0001380095,0.008167758,0.0009377651,0.0001142886,0.000002777659,0.00003176402,0.8508181,0.00008892715,0.00001974844,0.1384826,0.0002927484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4784302,0.1075071,0.3649993,0.01155842,0.004132332,0.02066551,0.000008291432,0.002615018,0.01008392],"genre_scores_gemma":[0.6101819,0.1787756,0.1257675,0.007304161,0.0005465088,0.005556165,0.0002705513,0.000167455,0.0714302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8816321,"threshold_uncertainty_score":0.9994854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0948412583542362,"score_gpt":0.3518979146421082,"score_spread":0.2570566562878721,"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."}}