{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115749"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115749","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A framework for intelligence augmented computing systems","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Banerjee, Subho Sankar"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Iyer, Ravishankar K","Hwu, Wen-mei","Adve, Vikram S","Mitra, Subhasish"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:55Z","subjects":["Machine Learning for Systems","Reinforcement learning","Bayesian Methods","Accelerators","Scheduling","Performance Monitoring","Error Correction"],"languages":["en","eng"],"rights":["Copyright 2022 Subho Banerjee"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115749","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Iyer, Ravishankar K","Hwu, Wen-mei","Adve, Vikram S","Mitra, Subhasish"]},{"key":"dc:creator","label":"Author","values":["Banerjee, Subho Sankar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-25"]},{"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 for Systems","Reinforcement learning","Bayesian Methods","Accelerators","Scheduling","Performance Monitoring","Error Correction"]}]},{"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 2022 Subho Banerjee"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115749"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Subho Banerjee, accepted the attached license on 2022-04-22 at 15:19.","The student, Subho Banerjee, submitted this Dissertation for approval on 2022-04-22 at 15:26.","This Dissertation was approved for publication on 2022-04-25 at 13:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17928 on 2022-11-11 at 12:58:12","Large-scale computing systems rely on many control and decision-making algorithms. Classical approaches to designing and optimizing these algorithms are poorly suited to the diverse and demanding requirements of modern systems and emerging applications. The state of the art paradigm for building these control algorithms often devolves into painstakingly built, handcrafted, average-case heuristics. However, as systems and applications have grown in complexity and heterogeneity, designing fixed algorithms that work well across a variety of conditions has become exceedingly difficult and costly. Moreover, we are reaching the limits of conventional approaches of generating heuristics, which involve recurring human-expert-driven engineering efforts. Such an approach will be untenable in the future. In this thesis, we investigate a new paradigm for solving large scale system management and optimization problems. We develop systems that can learn to optimize the performance on their own using modern machine learning techniques. As a result, in the proposed approach, the system designer need not develop specialized heuristics for low-level design goals. Instead, the designer architects a framework for measurement, estimation, experimentation, and learning that discovers the low-level actions that achieve high-level resource management objectives automatically. We use this approach to build a series of practical intelligent controllers for the management and optimization of large-scale data-parallel and data-processing workloads on heterogeneous computer systems. Our contributions encompass building mathematical models (e.g., for denoising telemetry data), policies (e.g., for scheduling), optimizations to enable real time inference, and the design and implementation of practical software and hardware that provides efficient, scalable, and composable system management solutions."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A framework for intelligence augmented computing systems"]}]}],"canonical_facts":{"dc:contributor":["Iyer, Ravishankar K","Hwu, Wen-mei","Adve, Vikram S","Mitra, Subhasish"],"dc:creator":["Banerjee, Subho Sankar"],"dc:date":["2022-05","2022-04-25"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Subho Banerjee, accepted the attached license on 2022-04-22 at 15:19.","The student, Subho Banerjee, submitted this Dissertation for approval on 2022-04-22 at 15:26.","This Dissertation was approved for publication on 2022-04-25 at 13:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17928 on 2022-11-11 at 12:58:12","Large-scale computing systems rely on many control and decision-making algorithms. Classical approaches to designing and optimizing these algorithms are poorly suited to the diverse and demanding requirements of modern systems and emerging applications. The state of the art paradigm for building these control algorithms often devolves into painstakingly built, handcrafted, average-case heuristics. However, as systems and applications have grown in complexity and heterogeneity, designing fixed algorithms that work well across a variety of conditions has become exceedingly difficult and costly. Moreover, we are reaching the limits of conventional approaches of generating heuristics, which involve recurring human-expert-driven engineering efforts. Such an approach will be untenable in the future. In this thesis, we investigate a new paradigm for solving large scale system management and optimization problems. We develop systems that can learn to optimize the performance on their own using modern machine learning techniques. As a result, in the proposed approach, the system designer need not develop specialized heuristics for low-level design goals. Instead, the designer architects a framework for measurement, estimation, experimentation, and learning that discovers the low-level actions that achieve high-level resource management objectives automatically. We use this approach to build a series of practical intelligent controllers for the management and optimization of large-scale data-parallel and data-processing workloads on heterogeneous computer systems. Our contributions encompass building mathematical models (e.g., for denoising telemetry data), policies (e.g., for scheduling), optimizations to enable real time inference, and the design and implementation of practical software and hardware that provides efficient, scalable, and composable system management solutions."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115749"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Subho Banerjee"],"dc:subject":["Machine Learning for Systems","Reinforcement learning","Bayesian Methods","Accelerators","Scheduling","Performance Monitoring","Error Correction"],"dc:title":["A framework for intelligence augmented computing systems"],"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":"2026-07-22T22:24:55Z"}