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- Yingyi Bu
- University of Washington, Seattle, WA
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- Bill Howe
- University of Washington, Seattle, WA
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- Magdalena Balazinska
- University of Washington, Seattle, WA
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- Michael D. Ernst
- University of Washington, Seattle, WA
書誌事項
- タイトル別名
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- efficient iterative data processing on large clusters
- 公開日
- 2010-09
- DOI
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- 10.14778/1920841.1920881
- 公開者
- Association for Computing Machinery (ACM)
この論文をさがす
説明
<jats:p>The growing demand for large-scale data mining and data analysis applications has led both industry and academia to design new types of highly scalable data-intensive computing platforms. MapReduce and Dryad are two popular platforms in which the dataflow takes the form of a directed acyclic graph of operators. These platforms lack built-in support for iterative programs, which arise naturally in many applications including data mining, web ranking, graph analysis, model fitting, and so on. This paper presents HaLoop, a modified version of the Hadoop MapReduce framework that is designed to serve these applications. HaLoop not only extends MapReduce with programming support for iterative applications, it also dramatically improves their efficiency by making the task scheduler loop-aware and by adding various caching mechanisms. We evaluated HaLoop on real queries and real datasets. Compared with Hadoop, on average, HaLoop reduces query runtimes by 1.85, and shuffles only 4% of the data between mappers and reducers.</jats:p>
収録刊行物
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- Proceedings of the VLDB Endowment
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Proceedings of the VLDB Endowment 3 (1-2), 285-296, 2010-09
Association for Computing Machinery (ACM)