Secure Data Dynamics in Tandem with Dynamic Resource Allocation

International Journal of Computer Trends and Technology (IJCTT)          
© - October Issue 2013 by IJCTT Journal
Volume-4 Issue-10                           
Year of Publication : 2013
Authors :Bindu Madhavi , G.Kalpana , Dr.R.V.Krishnaiah


Bindu Madhavi , G.Kalpana , Dr.R.V.Krishnaiah"Secure Data Dynamics in Tandem with Dynamic Resource Allocation"International Journal of Computer Trends and Technology (IJCTT),V4(10):3507-3511 October Issue 2013 .ISSN Published by Seventh Sense Research Group.

Abstract:- In Cloud computing both security with perfect data dynamics and optimal resource allocation are essential. For best realization of cloud computing parallel and reliable data processing is required. There are many providers of cloud services such as Oracle, Microsoft, IBM, and Google. The existing systems used for cloud computing are homogenous in nature. The resource allocation and execution of jobs parallelly has some limitations. The security is also concerns as the cloud servers are treated as un-trusted by the cloud users. In this paper parallel processing, dynamic resource allocation challenges are addressed. We built a prototype application to demonstrate the proof of concept and the empirical results are encouraging.


References -
[1] R. Chaiken, B. Jenkins, P.-A. Larson, B. Ramsey, D. Shakib,S. Weaver, and J. Zhou. SCOPE: Easy and Efficient ParallelProcessing of Massive Data Sets. Proc. VLDB Endow., 1(2):1265–1276, 2008.
[2] J. Dean and S. Ghemawat. MapReduce: Simplified Data Processingon Large Clusters. In OSDI’04: Proceedings of the 6th conferenceon Symposium on Opearting Systems Design & Implementation, pages
[3] M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: DistributedData-Parallel Programs from Sequential Building Blocks.In EuroSys ’07: Proceedings of the 2nd ACM SIGOPS/EuroSys EuropeanConference on Computer Systems 2007, pages 59–72, New York,NY, USA, 2007. ACM.
[4] H. chih Yang, A. Dasdan, R.-L. Hsiao, and D. S. Parker. Map-Reduce-Merge: Simplified Relational Data Processing on LargeClusters. In SIGMOD ’07: Proceedings of the 2007 ACM SIGMODinternational conference on Management of data, pages 1029–1040,New York, NY, USA, 2007. ACM.
[5] I. Raicu, I. Foster, and Y. Zhao. Many-Task Computing forGrids and Supercomputers. In Many-Task Computing on Grids andSupercomputers, 2008. MTAGS 2008. Workshop on, pages 1–11, Nov.2008.
[6] Amazon Web Services LLC. Amazon Elastic Compute Cloud (Amazon EC2)., 2009.
[7] The Apache Software Foundation. Welcome to Hadoop!, 2009.
[8] T. White. Hadoop: The Definitive Guide. O’Reilly Media, 2009.
[9] Amazon Web Services LLC. Amazon Elastic MapReduce., 2009.
[10] D. Warneke and O. Kao. Nephele: Efficient Parallel Data Processingin the Cloud. In MTAGS ’09: Proceedings of the 2nd Workshopon Many-Task Computing on Grids and Supercomputers, pages 1–10,New York, NY, USA, 2009. ACM.
[11] R. Pike, S. Dorward, R. Griesemer, and S. Quinlan. Interpretingthe Data: Parallel Analysis with Sawzall. Sci. Program., 13(4):277– 298, 2005.

Keywords :— Parallel processing, cloud computing, Map Reduce, many-task computing.