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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 8,August 2012
Efficient Dynamic Resource Allocation Using Nephele in a Cloud Environment
Full Text(PDF, )  PP.I056-I060  
Author(s)
V.Praveenkumar, Dr.S.Sujatha, R.Chinnasamy
KEYWORDS
IaaS, high-throughput computing, Nephele, Map Reduce
ABSTRACT
Today, Infrastructure-as-a-Service (IaaS) cloud providers have incorporated parallel data processing framework in their clouds for performing Many-task computing (MTC) applications. Parallel data processing framework reduces time and cost in processing the substantial amount of users' data. Nephele is a dynamic resource allocating parallel data processing framework, which is designed for dynamic and heterogeneous cluster environments. The existing framework does not support to monitor resource overload or under utilization, during job execution, efficiently. In this paper, we have proposed a framework based on Nephele, which aims to manage the resources automatically, while executing the job. Based on this framework, we have performed extended evaluations of Map Reduce-inspired data processing task, on an IaaS cloud system and compared the results with Nephele framework.
References
[1] Amazon Web Services LLC,” Amazon Elastic Compute Cloud|(Amazon EC2),”,htthttp://aws.amazon.com/ec2 /, 2012

[2] Amazon Web Services LLC, “Amazon Simple Storage Service,”http://aws.amazon.com/s3/ , 2012.

[3] R. Chaiken, B. Jenkins, P.-A. Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou, “SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets,” Proc. Very Large Database

[4] H. Chih Yang, A. Dasdan, R.-L. Hsiao, and D.S. Parker, “MapReduce-Merge: Simplified Relational Data Processing on Large Clusters,” Proc. ACM SIGMOD Int’l Conf. Management of Data, 2007

[5] J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” Proc. Sixth Conf. Symp. Operating Systems Design and Implementation (OSDI ’04), p. 10, 2004

[6] E. Deelman, G. Singh, M.-H. Su, J. Blythe, Y. Gil, C. Kesselman, G. Mehta, K. Vahi, G.B. Berriman, J. Good, A. Laity, J.C. Jacob, and D.S. Katz, “Pegasus: A Framework for Mapping Complex Scientific Workflows onto Distributed Systems,” Scientific Programming, vol. 13, no. 3, pp. 219-237, 2005.

[7] T. Dornemann, E. Juhnke, and B. Freisleben, “On-Demand Resource Provisioning for BPEL Workflows Using Amazon’s Elastic Compute Cloud,” Proc. Ninth IEEE/ACM Int’l Symp. Cluster Computing and the Grid (CCGRID ’09), pp. 140-147, 2009

[8] J. Frey, T. Tannenbaum, M. Livonia, I. Foster, and S. Tuecke, “Condor-G: A Computation Management Agent for MultiInstitutional Grids,” Cluster Computing, vol. 5, no. 3, pp. 237-246, 2002

[9] M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly, “Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks,” Proc. Second ACM SIGOPS/EuroSys European Conf.Computer Systems (EuroSys ’07), pp. 59-72, 2007[10]

[10] C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins, “Pig Latin: A Not-So-Foreign Language for Data Processing,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 1099-1110, 2008.

[11] R. Pike, S. Dorward, R. Griesemer, and S. Quinlan, “Interpreting the Data: Parallel Analysis with Sawzall,” Scientific Programming, vol. 13, no. 4, pp. 277-298, 2005.

[12] I. Raicu, I. Foster, and Y. Zhao, “Many-Task Computing for Grids and Supercomputers,” Proc. Workshop Many-Task Computing on Grids and Supercomputers, pp. 1-11, Nov. 2008.

[13] L. Ramakrishnan, C. Koelbel, Y.-S. Lee, R. Wolski, D. Nurmi, D. Gannon, G. Obertelli, A. YarKhan, A. Mandal, T.M. Huang, K. Thyagaraja, and D. Zagorodnov, “VGrADS: Enabling e-Science Workflows on Grids and Clouds with Fault Tolerance,” Proc. Conf. High Performance Computing Networking, Storage and Analysis (SC ’09), pp. 1-12, 2009.

[14] The Apache Software Foundation “Welcome to Hadoop!” http://hadoop.apache.org/,2012

[15] D. Warneke and O. Kao, “Nephele: Efficient Parallel Data Processing in the Cloud,” Proc. Second Workshop Many-Task Computing on Grids and Supercomputers (MTAGS ’09), pp. 1-10, 2009

[16] T. White, Hadoop: The Definitive Guide. O’Reilly Media, 2009. Endowment, vol. 1, no. 2, pp. 1265-1276, 2008.

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