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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
- Control Number
- 0017160740
- International Standard Book Number
- 9798383696118
- Dewey Decimal Classification Number
- 519
- Main Entry-Personal Name
- Sun, Xiao.
- Publication, Distribution, etc. (Imprint
- [S.l.] : State University of New York at Stony Brook., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 153 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- General Note
- Advisor: Liu, Zhenhua.
- Dissertation Note
- Thesis (Ph.D.)--State University of New York at Stony Brook, 2024.
- Summary, Etc.
- 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.
- Subject Added Entry-Topical Term
- Applied mathematics.
- Subject Added Entry-Topical Term
- Computer science.
- Subject Added Entry-Topical Term
- Systems science.
- Subject Added Entry-Topical Term
- Information technology.
- Index Term-Uncontrolled
- Scheduling algorithms
- Index Term-Uncontrolled
- Big data system
- Index Term-Uncontrolled
- Optimization
- Index Term-Uncontrolled
- Resource allocation
- Added Entry-Corporate Name
- State University of New York at Stony Brook Applied Mathematics and Statistics
- Host Item Entry
- Dissertations Abstracts International. 86-02B.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:657938
MARC
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■020 ▼a9798383696118
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aSun, Xiao.
■24510▼aResource Allocation and Scheduling Algorithms for Big Data Systems.
■260 ▼a[S.l.]▼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.
