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Resource Allocation and Scheduling Algorithms for Big Data Systems
Resource Allocation and Scheduling Algorithms for Big Data Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151109
- ISBN
- 9798383696118
- DDC
- 519
- 저자명
- Sun, Xiao.
- 서명/저자
- Resource Allocation and Scheduling Algorithms for Big Data Systems
- 발행사항
- [Sl] : State University of New York at Stony Brook, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 153 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Liu, Zhenhua.
- 학위논문주기
- Thesis (Ph.D.)--State University of New York at Stony Brook, 2024.
- 초록/해제
- 요약Big data is an omnipresent force in today's world. A crucial aim for numerous contemporary businesses and scientific endeavors is to harness and utilize as much information as they can, as swiftly as possible. However, without the right resource allocation and scheduling algorithms, systems struggle to deliver satisfactory services to millions with diverse needs. This thesis introduces innovative algorithms and frameworks specifically designed for resource allocation and scheduling in big data systems, addressing various levels of granularity. Ranging from multi-user environments to individual users and singular jobs, the proposed methodologies not only boost performance but also maintain fairness and efficiency across these systems. Furthermore, our approach effectively bridges the gap between theory and practical applications. Through extensive evaluation using real-world data on popular platforms, our methods demonstrate significant improvements over existing solutions. The findings of this thesis have gained recognition, being published in top system conferences such as EuroSys, SIGCOMM, and Middleware.
- 일반주제명
- Applied mathematics
- 일반주제명
- Computer science
- 일반주제명
- Systems science
- 일반주제명
- Information technology
- 키워드
- Big data system
- 키워드
- Optimization
- 기타저자
- State University of New York at Stony Brook Applied Mathematics and Statistics
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798383696118
■035 ▼a(MiAaPQ)AAI31144012
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aSun, Xiao.
■24510▼aResource Allocation and Scheduling Algorithms for Big Data Systems
■260 ▼a[Sl]▼bState University of New York at Stony Brook▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a153 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Liu, Zhenhua.
■5021 ▼aThesis (Ph.D.)--State University of New York at Stony Brook, 2024.
■520 ▼aBig data is an omnipresent force in today's world. A crucial aim for numerous contemporary businesses and scientific endeavors is to harness and utilize as much information as they can, as swiftly as possible. However, without the right resource allocation and scheduling algorithms, systems struggle to deliver satisfactory services to millions with diverse needs. This thesis introduces innovative algorithms and frameworks specifically designed for resource allocation and scheduling in big data systems, addressing various levels of granularity. Ranging from multi-user environments to individual users and singular jobs, the proposed methodologies not only boost performance but also maintain fairness and efficiency across these systems. Furthermore, our approach effectively bridges the gap between theory and practical applications. Through extensive evaluation using real-world data on popular platforms, our methods demonstrate significant improvements over existing solutions. The findings of this thesis have gained recognition, being published in top system conferences such as EuroSys, SIGCOMM, and Middleware.
■590 ▼aSchool code: 0771.
■650 4▼aApplied mathematics
■650 4▼aComputer science
■650 4▼aSystems science
■650 4▼aInformation technology
■653 ▼aScheduling algorithms
■653 ▼aBig data system
■653 ▼aOptimization
■653 ▼aResource allocation
■690 ▼a0364
■690 ▼a0489
■690 ▼a0984
■690 ▼a0790
■71020▼aState University of New York at Stony Brook▼bApplied Mathematics and Statistics.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0771
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160740▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
