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Multi-Objective Resource Optimization for Large Scale Machine Learning Systems
Multi-Objective Resource Optimization for Large Scale Machine Learning Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105658
- ISBN
- 9798263307417
- DDC
- 004
- 저자명
- Guo, Hongpeng.
- 서명/저자
- Multi-Objective Resource Optimization for Large Scale Machine Learning Systems
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 118 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Nahrstedt, Klara.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
- 초록/해제
- 요약The recent advancements in machine learning (ML) have marked significant progress across various fields, delivering high-quality solutions in computer vision, natural language processing, and virtual reality, among others. This leap forward is largely due to the innovations in deep learning and neural networks, which have opened new avenues in data analysis and decision-making processes, profoundly affecting people's lives and how society functions. However, ML systems encounter considerable challenges in terms of resource efficiency, grappling with complex issues such as network bandwidth exhaustion, computational intensity, energy consumption, and hardware diversity. These challenges are interconnected, making the task of managing resources efficiently even more daunting. To enhance the effectiveness of machine learning models, it's crucial that these systems are optimized across multiple dimensions to strike a balance between performance, efficiency, and scalability, thereby ensuring sustainable operation at a larger scale. In this thesis, we introduce a multi-objective resource optimization framework aimed at addressing the overarching resource challenges in large-scale machine learning systems. Leveraging the optimization opportunities presented by data redundancy and hardware configurability, we detail three initiatives that demonstrate optimizations for resource constraints within large-scale ML systems. Specifically, CrossRoI tackles network bandwidth and computational intensity by leveraging data redundancy in video streams, significantly reducing the amount of data required for processing and transmission. BoFL targets energy consumption and the timeliness of learning tasks, employing dynamic hardware configuration to enhance the power efficiency of devices involved in time-sensitive federated learning, which in turn prolongs battery life and lowers operational expenses. FedCore addresses the straggler effect in federated learning through the implementation of distributed coresets, minimizing the data processed by slower devices and thus boosting the overall efficiency of the system without sacrificing accuracy. Collectively, these frameworks embody a comprehensive approach to multi-objective resource optimization, illustrating their effectiveness through significant enhancements across various resource dimensions. Moreover, our experiments confirm that adopting a holistic design that leverages both data and hardware opportunities can substantially elevate the efficiency of resource usage in machine learning systems.
- 일반주제명
- Computer science
- 키워드
- Machine learning
- 기타저자
- University of Illinois at Urbana-Champaign Computer Science
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798263307417
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■035 ▼a(MiAaPQ)124238
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aGuo, Hongpeng.
■24510▼aMulti-Objective Resource Optimization for Large Scale Machine Learning Systems
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a118 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Nahrstedt, Klara.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
■520 ▼aThe recent advancements in machine learning (ML) have marked significant progress across various fields, delivering high-quality solutions in computer vision, natural language processing, and virtual reality, among others. This leap forward is largely due to the innovations in deep learning and neural networks, which have opened new avenues in data analysis and decision-making processes, profoundly affecting people's lives and how society functions. However, ML systems encounter considerable challenges in terms of resource efficiency, grappling with complex issues such as network bandwidth exhaustion, computational intensity, energy consumption, and hardware diversity. These challenges are interconnected, making the task of managing resources efficiently even more daunting. To enhance the effectiveness of machine learning models, it's crucial that these systems are optimized across multiple dimensions to strike a balance between performance, efficiency, and scalability, thereby ensuring sustainable operation at a larger scale. In this thesis, we introduce a multi-objective resource optimization framework aimed at addressing the overarching resource challenges in large-scale machine learning systems. Leveraging the optimization opportunities presented by data redundancy and hardware configurability, we detail three initiatives that demonstrate optimizations for resource constraints within large-scale ML systems. Specifically, CrossRoI tackles network bandwidth and computational intensity by leveraging data redundancy in video streams, significantly reducing the amount of data required for processing and transmission. BoFL targets energy consumption and the timeliness of learning tasks, employing dynamic hardware configuration to enhance the power efficiency of devices involved in time-sensitive federated learning, which in turn prolongs battery life and lowers operational expenses. FedCore addresses the straggler effect in federated learning through the implementation of distributed coresets, minimizing the data processed by slower devices and thus boosting the overall efficiency of the system without sacrificing accuracy. Collectively, these frameworks embody a comprehensive approach to multi-objective resource optimization, illustrating their effectiveness through significant enhancements across various resource dimensions. Moreover, our experiments confirm that adopting a holistic design that leverages both data and hardware opportunities can substantially elevate the efficiency of resource usage in machine learning systems.
■590 ▼aSchool code: 0090.
■650 4▼aComputer science
■653 ▼aMachine learning
■653 ▼aDistributed systems
■653 ▼aPerformance optimization
■653 ▼aNatural language processing
■690 ▼a0984
■690 ▼a0800
■71020▼aUniversity of Illinois at Urbana-Champaign▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361049▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
