{"id":"W4401983721","doi":"10.1007/978-3-031-67195-1_80","title":"Advancing Scalability and Efficiency in Distributed Network Computing Through Innovative Resource Allocation and Load Balancing Strategies","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sheridan College","funders":"","keywords":"Scalability; Computer science; Distributed computing; Load balancing (electrical power); Resource allocation; Resource (disambiguation); Computer network; Database; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001545642,0.0004708221,0.0006921469,0.000131037,0.000211503,0.0006493136,0.0002820747,0.0003787501,3.78262e-7],"category_scores_gemma":[0.00006365821,0.0004061843,0.0000376745,0.0005610537,0.0001578628,0.00004430385,0.0007377129,0.0009521218,3.852174e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319838,"about_ca_system_score_gemma":0.00006562679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002511156,"about_ca_topic_score_gemma":0.0002776267,"domain_scores_codex":[0.9971939,0.0001217334,0.0007553251,0.001069064,0.0003145154,0.0005454915],"domain_scores_gemma":[0.9983016,0.0008487363,0.0002860802,0.0004011496,0.00009765087,0.00006473431],"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":[0.00000783924,0.000008094993,0.001044907,0.0004317743,0.00002835337,0.00003750007,0.001538004,0.9199877,0.000001024396,0.05751528,0.000035419,0.01936407],"study_design_scores_gemma":[0.0002461057,0.00007265467,0.000841919,0.003729727,0.00001445052,0.00003766974,0.0001031466,0.9750682,3.301815e-7,0.01668918,0.002773067,0.0004235137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02464467,0.04648435,0.9210241,0.0005079379,0.000787312,0.0008772255,0.000003598562,0.0001982258,0.005472614],"genre_scores_gemma":[0.9982267,0.0001485392,0.0007785758,0.0001007996,0.0005183995,0.000006831215,0.00001251083,0.00003021813,0.0001774063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.973582,"threshold_uncertainty_score":0.999839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008243175330796127,"score_gpt":0.2295028470638618,"score_spread":0.2212596717330657,"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."}}