PPT-MapReduce
Author : danika-pritchard | Published Date : 2016-05-27
Simplified Data Processing on Large Clusters by Jeffrey Dean and Sanjay Ghemawa Presented by Jon Logan Outline Problem Statement Motivation An Example Program
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MapReduce: Transcript
Simplified Data Processing on Large Clusters by Jeffrey Dean and Sanjay Ghemawa Presented by Jon Logan Outline Problem Statement Motivation An Example Program MapReduce vs Hadoop GFS HDFS. 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). 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. Paradigms Activity at Data Intensive Workshop. . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu. 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 : . Yang . Ruan. , . Zhenhua . Guo. , . Yuduo. Zhou, Judy . Qiu. , Geoffrey Fox. Indiana . University, US. Outline. Introduction and Background. MapReduce. Iterative MapReduce. Distributed Workflow . Management Systems. By . Yufei. Tao, . Wenqing. . Lin, . Xiaokui. . Xia. Edited by Tuval Rotem. First topic:. What is MapReduce. But first…. Regular day at work. You’re doing your regular work while suddenly your boss, . . 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. Jimmy Lin. The iSchool. University of Maryland. Monday, March 30, 2009. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States. See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details. 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. MapReduce. Architecture. MapReduce. Internals. MapReduce. Examples. JobTracker. Interface. MapReduce. : A Real World Analogy. Coins Deposit. ?. MapReduce. : A Real World Analogy. Coins Deposit. Coins Counting Machine.
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