PPT-MapReduce

Author : min-jolicoeur | Published Date : 2016-08-13

the Big Data Workhorse Vyassa Baratham Stony Brook University April 20 2013 105205pm cSplash 2013 Use several computers to process large amounts of data Often significant

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MapReduce: Transcript

the Big Data Workhorse Vyassa Baratham Stony Brook University April 20 2013 105205pm cSplash 2013 Use several computers to process large amounts of data Often significant distribution overhead. ：. R. eusing . results . of . mapreduce. . jobs. Jun Fan. Outline. Introduction to . MapReduce. MapReduce. and its implementations such as . Hadoop. are common in . Facebook. , Yahoo, and Google as well as smaller companies. : Simplified Data Processing on Large Clusters. Jeffrey Dean & . Sanjay . Ghemawat. Appeared in:. OSDI '04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December, 2004. . Tsung. -Wei Huang. and Martin D. F. Wong. Department of Electrical and Computer Engineering (ECE). University of Illinois at Urbana-Champaign (UIUC), IL, USA. 2015 ACM International Symposium on Physical Design (ISPD). Cloud, Part II: Search => Cluster Apps => Scalable Machine Learning. David E. Culler. . CS162 – Operating Systems and Systems Programming. Lecture 40. December 3, . 2014. Proj. : CP 2 . today. Runhui Li, . Patrick P. C. Lee. , . Yuchong. Hu. The Chinese University of Hong Kong. DSN’14. Motivation. Clustered storage systems. . are widely . deployed . (e.g. ., GFS, HDFS, Azure). Stripe data across multiple nodes. Based on the text by Jimmy Lin and Chris Dryer; and on the yahoo tutorial on . mapreduce. at . http://developer.yahoo.com/hadoop/tutorial/index.html. Namenode responsibilities:. Namespace management: file name, locations, access privileges etc.. IUPUI Computer Science. February 11 2011. Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . Director, Digital Science Center, Pervasive Technology Institute. , Collective Communication, and Services. Oral Exam, . Bingjing. Zhang. Outline. MapReduce. MapReduce. Frameworks. Iterative . MapReduce. Frameworks. Frameworks Based on . MapReduce. and Alternatives. Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . Parallel Computing. MapReduce. Examples. Parallel Efficiency. Assignment. Parallel Computing. Parallel efficiency with . p. processors. Traditional parallel computing:. focus on compute intensive tasks. Jaliya Ekanayake. Community Grids Laboratory, . Digital Science Center. Pervasive Technology Institute. Indiana University. HPDC – 2010 MAPREDUCE’10 Workshop, Chicago, 06/22/2010. Acknowledgements to:. Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu/. . Director, Digital Science Center, Pervasive Technology Institute. Source. MapReduce. : Simplified Data Processing in Large Clusters. . Jefferey. Dean and Sanjay . Ghemawat. . OSDI 2004. Example Scenario. 3. Genome data from roughly . one million users. 125 MB of data per user.

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