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Resource Management for Advanced Data Analytics at Large Scale
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Resource Management for Advanced Data Analytics at Large Scale. |
| 개인저자 | Zhang, Haoyu. |
| 단체저자명 | Princeton University. Computer Science. |
| 발행사항 | [S.l.]: Princeton University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 149 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438049635 |
| 학위논문주기 | Thesis (Ph.D.)--Princeton University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Michael J. Freedman. |
| 요약 | The rapidly growing size of data and the complexity of analytics present new challenges for large-scale data analytics systems. Modern distributed computing frameworks need to support not only embarrassingly parallelizable batch jobs, but also a |
| 요약 | In this thesis, we present resource management systems that significantly improve cloud resource efficiency by leveraging the specific characteristics of advanced data analytics applications. We present the design and implementation of the follo |
| 요약 | (i) VideoStorm: a video analytics system that scales to process thousands of vision queries on live video streams over large clusters. VideoStorm's offline profiler generates resource-quality profiles for vision queries, and its online scheduler |
| 요약 | (ii) SLAQ: a cluster scheduling system for approximate ML training jobs that aims to maximize the overall model quality. In iterative and exploratory training settings, better models can be obtained faster by directing resources to jobs with the |
| 요약 | (iii) Riffle: an optimized shuffle service for big-data analytics frameworks that significantly improves I/O efficiency. The all-to-all data transfer (i.e., shuffle) in modern big-data systems (such as Spark and Hadoop) becomes the scaling bottl |
| 요약 | Taken together, this thesis demonstrates a novel set of methods in both job-level and task-level scheduling for building scalable, highly-efficient, and cost-effective resource management systems. We have performed extensive evaluation with real |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
