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An Enhanced Map Reduce Framework for Improving the Performance of Massively Scalable Private Clouds

Published on January 2013 by Ashutosh Rajan, M. V. Judy
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Amrita International Conference of Women in Computing - 2013
Foundation of Computer Science USA
AICWIC - Number 3
January 2013
Authors: Ashutosh Rajan, M. V. Judy
1e38806d-8fe2-480b-b67f-d3cf849a69b7

Ashutosh Rajan, M. V. Judy . An Enhanced Map Reduce Framework for Improving the Performance of Massively Scalable Private Clouds. Amrita International Conference of Women in Computing - 2013. AICWIC, 3 (January 2013), 24-26.

@article{
author = { Ashutosh Rajan, M. V. Judy },
title = { An Enhanced Map Reduce Framework for Improving the Performance of Massively Scalable Private Clouds },
journal = { Amrita International Conference of Women in Computing - 2013 },
issue_date = { January 2013 },
volume = { AICWIC },
number = { 3 },
month = { January },
year = { 2013 },
issn = 0975-8887,
pages = { 24-26 },
numpages = 3,
url = { /proceedings/aicwic/number3/9878-1320/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Amrita International Conference of Women in Computing - 2013
%A Ashutosh Rajan
%A M. V. Judy
%T An Enhanced Map Reduce Framework for Improving the Performance of Massively Scalable Private Clouds
%J Amrita International Conference of Women in Computing - 2013
%@ 0975-8887
%V AICWIC
%N 3
%P 24-26
%D 2013
%I International Journal of Computer Applications
Abstract

Cloud Computing systems provide access to large amount of data and other resources through a large number of interfaces. Apache Hadoop is a framework that allows distributed processing of large sets of data across cluster of computers. It is a powerful abstraction proposed for making scalable and fault tolerant applications. In this paper we have suggested an enhanced framework for MapReduce which increased the performance of the Private Clouds in distributed environment. In this framework a separate thread is maintained for each and every Mapper and a single buffer is used for retrieving all threads. A single Buffer retrieves all records. At this instance a separate thread can search for all the records with same key in the buffer and pass it on to the Reduce function which can executed. A multimap is used to access partial result while maintain key ordering. Our analysis shows that better performance can be achieved using this enhanced Map Reduce framework than using the traditional MapReduce framework. The results show the reduction in job completion time when compared with existing one.

References
  1. Private Cloud setup http://www. akashsharma. me/private-cloud-setup-using-eucalyptus-and-xen/
  2. J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. Commun. ACM, 51(1):107–113,2008.
  3. The Apache Hadoop Project. http://hadoop. apache. org/core/, 2009.
  4. Data Intensive applications http://en. wikipedia. org/wiki/Data_Intensive_Computing#MapReduce
  5. Fundamentals of cloud computing http://www. cse. fau. edu/~borko/HandbookofCloudComputing.
  6. Cloud computing http://en. wikipedia. org/wiki/Cloud_computing
  7. Eucalyptus Beginner's Guide UEC Edition by Johnson D, Kiran Murari, Murthy Raju, Suseendran RB, Yogesh Girikumar.
  8. Apache Hadoop Map Reduce http://hadoop. apache. org/common/docs/current/mapred_tutorial. html#MapReduce+- +User+Interfaces
  9. Apache Hadoop Distributed File System http://hadoop. apache. org/common/docs/current/hdfs_design. html
  10. Running Hadoop on single node environment http://www. michael-noll. com/tutorials/running-hadoop-on-ubuntu-linux-single-node-cluster/
  11. Running Hadoop on multi node environment http://www. michael-noll. com/tutorials/running-hadoop-on-ubuntu-linux-multi-node-cluster/
  12. R. Raghu Raman, A. Penmetsa, G. Bradski, and C. Kozyrakis. Evaluating mapreduce for multi-core and multiprocessor systems. Proceedings of the 2007 IEEE 13th International Symposium
  13. Breaking the Map Reduce stage Barriers
  14. Abhishek Verma, Nicolas Zea, Brian Cho, Indranil Gupta, Roy H. Campbell University of Illinois at Urbana-Champaign
Index Terms

Computer Science
Information Sciences

Keywords

Enhanced Map