An increasing number of popular applications become data-intensive in nature. In the past decade, the World Wide Web has been adopted as an ideal platform for developing data-intensive applications, since the communication paradigm of the Web is sufficiently open and powerful. Data-intensive applications like data mining and web indexing need to access ever-expanding data sets ranging from a few gigabytes to several terabytes or even petabytes. Google leverages the MapReduce model to process approximately twenty petabytes of data per day in a parallel fashion. In this talk, we introduce the Google’s MapReduce framework for processing huge datasets on large clusters. We first outline the motivations of the MapReduce framework. Then, we describe the dataflow of MapReduce. Next, we show a couple of example applications of MapReduce. Finally, we present our research project on the Hadoop Distributed File System. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into account for launching speculative map tasks, because it is assumed that most maps are data-local. Unfortunately, both the homogeneity and data locality assumptions are not satisfied in virtualized data centers. We show that ignoring the datalocality issue in heterogeneous environments can noticeably reduce the MapReduce performance. In this paper, we address the problem of how to place data across nodes in a way that each node has a balanced data processing load. Given a dataintensive application running on a Hadoop MapReduce cluster, our data placement scheme adaptively balances the amount of data stored in each node to achieve improved data-processing performance. Experimental results on two real data-intensive applications show that our data placement strategy can always improve the MapReduce performance by rebalancing data across nodes before performing a data-intensive application in a heterogeneous Hadoop cluster.

1. HDFS-HC: A Data Placement Module for Heterogeneous Hadoop Clusters 02/28/11 Xiao Qin Department of Computer Science and Software Engineering Auburn University http://www.eng.auburn.edu/~xqin [email_address] Slides 2-20 are adapted from notes by Subbarao Kambhampati (ASU), Dan Weld (U. Washington), Jeff Dean, Sanjay Ghemawat, (Google, Inc.)

4. Distributed Grep Very big data Split data Split data Split data Split data grep grep grep grep matches matches matches matches cat All matches

5. Distributed Word Count Very big data Split data Split data Split data Split data count count count count count count count count merge merged count

13. Model is Widely Applicable MapReduce Programs In Google Source Tree Example uses: distributed grep distributed sort web link-graph reversal term-vector / host web access log stats inverted index construction document clustering machine learning statistical machine translation ... ... ...

17. Execution

18. Parallel Execution

21. Improving MapReduce Performance through Data Placement in Heterogeneous Hadoop Clusters Download Software at: http://www.eng.auburn.edu/~xqin/software/hdfs-hc This HDFS-HC tool was described in our paper - Improving MapReduce Performance via Data Placement in Heterogeneous Hadoop Clusters - by J. Xie, S. Yin, X.-J. Ruan, Z.-Y. Ding, Y. Tian, J. Majors, and X. Qin, published in Proc. 19th Int'l Heterogeneity in Computing Workshop, Atlanta, Georgia, April 2010.

22. Hadoop Overview (J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters. OSDI ’04, pages 137–150, 2008)

24. Hadoop Distributed File System (http://lucene.apache.org/hadoop)

25. Motivational Example Time (min) Node A (fast) Node B (slow) Node C (slowest) 2x slower 3x slower 1 task/min

26. The Native Strategy Node A Node B Node C 3 tasks 2 tasks 6 tasks Loading Transferring Processing Time (min)

27. Our Solution --Reducing data transfer time Node A’ Node B’ Node C’ 3 tasks 2 tasks 6 tasks Loading Transferring Processing Time (min) Node A

28. Preliminary Results Impact of data placement on performance of grep

31. Steps to Measure Computing Ratios 1. Run the application on each node with the same size data, individually collect the response time 2. Set the ratio of the shortest response as 1, accordingly set the ratio of other nodes 3.Caculate the least common multiple of these ratios 4. Count the portion of each node Node Response time(s) Ratio # of File Fragments Speed Node A 10 1 6 Fastest Node B 20 2 3 Average Node C 30 3 2 Slowest

35. Experimental Environment Five nodes in a hadoop heterogeneous cluster Node CPU Model CPU(Hz) L1 Cache(KB) Node A Intel core 2 Duo 2*1G=2G 204 Node B Intel Celeron 2.8G 256 Node C Intel Pentium 3 1.2G 256 Node D Intel Pentium 3 1.2G 256 Node E Intel Pentium 3 1.2G 256

37. Computing ratio for two applications Computing ratio of the five nodes with respective of Grep and Wordcount applications Computing Node Ratios for Grep Ratios for Wordcount Node A 1 1 Node B 2 2 Node C 3.3 5 Node D 3.3 5 Node E 3.3 5

38. Response time of Grep and wordcount in each Node Application dependence Data size independence

39. Six Data Placement Decisions

40. Impact of data placement on performance of Grep

41. Impact of data placement on performance of WordCount

45. http://www.eng.auburn.edu/programs/grad-school/fellowship-program/

46. http://www.eng.auburn.edu

47. http://www.eng.auburn.edu

48. http://www.eng.auburn.edu

49. Download the presentation slides http://www.slideshare.net/xqin74 Google: slideshare Xiao Qin

50. Questions