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Resource Management for Advanced Data Analytics at Large Scale

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개인저자Zhang, Haoyu.
단체저자명Princeton University. Computer Science.
서명/저자사항Resource Management for Advanced Data Analytics at Large Scale.
발행사항[S.l.] : Princeton University., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항149 p.
소장본 주기School code: 0181.
ISBN9780438049635
일반주기 Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Michael J. Freedman.
요약The 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
요약In 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
요약Taken 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
일반주제명Computer science.
언어영어
기본자료 저록Dissertation Abstracts International79-10B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14998602

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