{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125692"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125692","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning systems in constrained environments","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","abstract_has_math":false,"creators":["Jeon, Beomyeol"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Gupta, Indranil","Caesar, Matthew","Park, Yongjoo","Wang, Chen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-08","date_published":"2024-07-08","updated_at":"2026-07-22T22:25:02Z","subjects":["Machine Learning Systems","Machine Learning","Constraints","Placements","Autoscaling","Graph Neural Networks","Serverless Computing","Optimizations","Algorithms"],"languages":["en","eng"],"rights":["Copyright 2024 Beomyeol Jeon"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125692","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gupta, Indranil","Caesar, Matthew","Park, Yongjoo","Wang, Chen"]},{"key":"dc:creator","label":"Author","values":["Jeon, Beomyeol"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-08","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning Systems","Machine Learning","Constraints","Placements","Autoscaling","Graph Neural Networks","Serverless Computing","Optimizations","Algorithms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Beomyeol Jeon"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125692"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","The student, Beomyeol Jeon, accepted the attached license on 2024-07-04 at 12:16.","The student, Beomyeol Jeon, submitted this Dissertation for approval on 2024-07-04 at 12:26.","This Dissertation was approved for publication on 2024-07-08 at 12:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20949 on 2025-02-04 at 21:16:21","Machine learning (ML) training and inference systems encounter constraints in current computation environments due to increased ML model sizes, the fast-growing popularity of ML/AI, etc. In this thesis, we show how machine learning training and inference systems can be executed successfully and efficiently in constrained computation environments, such as limited-memory GPUs, on-premises clusters, and serverless environments, by using a novel combination of algorithms, optimizations, and well-reasoned system designs. Concretely, we propose (i) a system that enables large ML model training over multiple memory-constrained GPU devices via algorithms and system designs that achieve fast placements with a quality comparable to expert-designed placements, (ii) a system that enables efficient resource sharing among ML inference jobs in fixed-size on-premises clusters by making close-to-optimal autoscaling decisions quickly via several relaxation methods in optimization and prediction, and (iii) a system that enables cost-efficient distributed GNN training on constrained serverless execution environments by auto-tuning configuration via analytic model-based offline optimization and gray-box heuristic-based online optimization."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine learning systems in constrained environments"]}]}],"canonical_facts":{"dc:contributor":["Gupta, Indranil","Caesar, Matthew","Park, Yongjoo","Wang, Chen"],"dc:creator":["Jeon, Beomyeol"],"dc:date":["2024-07-08","2024-08"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","The student, Beomyeol Jeon, accepted the attached license on 2024-07-04 at 12:16.","The student, Beomyeol Jeon, submitted this Dissertation for approval on 2024-07-04 at 12:26.","This Dissertation was approved for publication on 2024-07-08 at 12:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20949 on 2025-02-04 at 21:16:21","Machine learning (ML) training and inference systems encounter constraints in current computation environments due to increased ML model sizes, the fast-growing popularity of ML/AI, etc. In this thesis, we show how machine learning training and inference systems can be executed successfully and efficiently in constrained computation environments, such as limited-memory GPUs, on-premises clusters, and serverless environments, by using a novel combination of algorithms, optimizations, and well-reasoned system designs. Concretely, we propose (i) a system that enables large ML model training over multiple memory-constrained GPU devices via algorithms and system designs that achieve fast placements with a quality comparable to expert-designed placements, (ii) a system that enables efficient resource sharing among ML inference jobs in fixed-size on-premises clusters by making close-to-optimal autoscaling decisions quickly via several relaxation methods in optimization and prediction, and (iii) a system that enables cost-efficient distributed GNN training on constrained serverless execution environments by auto-tuning configuration via analytic model-based offline optimization and gray-box heuristic-based online optimization."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125692"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Beomyeol Jeon"],"dc:subject":["Machine Learning Systems","Machine Learning","Constraints","Placements","Autoscaling","Graph Neural Networks","Serverless Computing","Optimizations","Algorithms"],"dc:title":["Machine learning systems in constrained environments"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}