International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 4 | Issue 10 | Year 2013 | Article Id. IJCTT-V4I10P124 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I10P124

Secure Data Dynamics in Tandem with Dynamic Resource Allocation


Bindu Madhavi , G.Kalpana , Dr.R.V.Krishnaiah

Citation :

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), vol. 4, no. 10, pp. 3507-3511, 2013. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V4I10P124

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.

Keywords

Parallel processing, cloud computing, Map Reduce, many-task computing.

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. MapReduce-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 111, Nov.2008.
[6] Amazon Web Services LLC. Amazon Elastic Compute Cloud (Amazon EC2). http://aws.amazon.com/ec2/, 2009.
[7] The Apache Software Foundation. Welcome to Hadoop! http://hadoop.apache.org/, 2009.
[8] T. White. Hadoop: The Definitive Guide. O’Reilly Media, 2009.
[9] Amazon Web Services LLC. Amazon Elastic MapReduce. http://aws.amazon.com/elasticmapreduce/, 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.
[12] C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins.Pig Latin: A Not-So-Foreign Language for Data Processing. InSIGMOD ’08: Proceedings of the 2008 ACM SIGMOD internationalconference on Management of data, pages 1099–1110, New York, NY,USA, 2008. ACM.
[13] 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 ComplexScientific Workflows onto Distributed Systems. Sci. Program.,13(3):219–237, 2005.
[14] J. Frey, T. Tannenbaum, M. Livny, I. Foster, and S. Tuecke. Condor-G: A Computation Management Agent for MultiInstitutionalGrids. Cluster Computing, 5(3):237–246, 2002.
[15] Y. Zhao, M. Hategan, B. Clifford, I. Foster, G. von Laszewski,V. Nefedova, I. Raicu, T. Stef-Praun, and M. Wilde. Swift: Fast,Reliable, Loosely Coupled Parallel Computation. In Services, 2007IEEE Congress on, pages 199–206, July 2007. [16] I. Foster and C. Kesselman. Globus: A Metacomputing InfrastructureToolkit. Intl. Journal of Supercomputer Applications, 11(2):115–128, 1997.
[17] I. Raicu, Y. Zhao, C. Dumitrescu, I. Foster, and M. Wilde. Falkon:a Fast and Light-weight tasK executiON framework. In SC ’07:Proceedings of the 2007 ACM/IEEE conference on Supercomputing,pages 1–12, New York, NY, USA, 2007. ACM.
[18] G. von Laszewski, M. Hategan, and D. Kodeboyina. Workflowsfor e-Science Scientific Workflows for Grids. Springer, 2007.
[19] T. Dornemann, E. Juhnke, and B. Freisleben. On-Demand ResourceProvisioning for BPEL Workflows Using Amazon’s ElasticCompute Cloud. In CCGRID ’09: Proceedings of the 2009 9thIEEE/ACM International Symposium on Cluster Computing and theGrid, pages 140–147, Washington, DC, USA, 2009. IEEE ComputerSociety.
[20] L. Ramakrishnan, C. Koelbel, Y.-S. Kee, R. Wolski, D. Nurmi,D. Gannon, G. Obertelli, A. YarKhan, A. Mandal, T. M. Huang,K. Thyagaraja, and D. Zagorodnov. VGrADS: Enabling eScienceWorkflows on Grids and Clouds with Fault Tolerance. In SC’09: Proceedings of the Conference on High Performance ComputingNetworking, Storage and Analysis, pages 1–12, New York, NY, USA,2009. ACM.
[21] D. Wentzlaff, C. G. III, N. Beckmann, K. Modzelewski, A. Belay,L. Youseff, J. Miller, and A. Agarwal. An Operating System forMulticore and Clouds: Mechanisms and Implementation. In SoCC’10: Proceedings of the ACM Symposium on Cloud Computing 2010,pages 3–14, New York, NY, USA, 2010. ACM.
[22] D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman,L. Youseff, and D. Zagorodnov. Eucalyptus: A Technical Report onan Elastic Utility Computing Architecture Linking Your Programsto Useful Systems. Technical report, University of California,Santa Barbara, 2008.
[23] R. Russell. virtio: Towards a De-Facto Standard for Virtual I/ODevices. SIGOPS Oper. Syst. Rev., 42(5):95–103, 2008.