Role of Cloud Computing in Cost Minimization: A Review

Pramod Kumar Sagar; Kanika Garg Assistant; Ruby Singh. Assistant

Abstract

Cloud computing has rapidly emerged as a new computation paradigm, providing agile and scalable resource access in a utility-like fashion. Processing of massive amounts of data has been a primary usage of the clouds in practice. While many efforts have been devoted to designing the computation models, one important issue has been largely neglected in this respect: how do we efficiently move the data, practically generated from different geographical locations over time, into a cloud for effective processing? The usual approach of shipping data using hard disks lacks flexibility and security. As the first dedicated effort, this paper tackles this massive, dynamic data migration issue. Targeting a cloud encompassing disparate data centers of different resource charges, we model the cost-minimizing data migration problem, and propose efficient offline and online algorithms, which optimize the routes of data into the cloud and the choice of the data center to aggregate the data for processing, at any given time. This study is focusing on various issues in cloud computing and some suggestions and conclusion of the study

Keywords

Cloud Computing, Utility Computing, Internet Datacenters, Distributed System Economics.

Reference

[1]E. E. Schadt, M. D. Linderman, J. Sorenson, L. Lee, and G. P. Nolan, “Computational Solutions to Large-scale Data Management and Analysis,” Nat Rev Genet,vol. 11, no. 9, pp. 647–657, 09 2010.

[2] Moving an Elephant: Large Scale Hadoop Data Migration at Facebook http://www.facebook.com/notes/paul-yang/ movingan-elephant-large-scalehadoop-data-migration-atfacebook/10150246275318920.

[3] “A Conversation with Jim Gray,” Queue, vol. 1, no. 4, pp. 8–17, Jun. 2003.

[4] R. J. Brunner, S. G. Djorgovski, T. A. Prince, and A. S. Szalay, “Handbook of MassiveData Sets,” J. Abello, P. M. Pardalos, and M. G. C. Resende, Eds. Norwell,MA, USA: Kluwer Academic Publishers, 2002, ch. Massive Datasets in Astronomy,pp. 931– 979.

[5] SenseWeb, http://research. microsoft. com/ en-us/ projects/ senseweb/.

[6] Amazon Elastic MapReduce, http://aws.amazon.com/elasticmapreduce/.

[7]“Cloud Computing ,” National Institue of Standards and Technology, http://www.nist.gov/ itl/ cloud/ index.cfm.

[8] A. Borodin and R. El-Yaniv, Online Computation and Competitive Analysis.Cambridge University Press, 998, vol. 2.

[9] A. Borodin, N. Linial, and M. E. Saks, “An Optimal On-line Algorithm for MetricalTask System,” J. ACM, vol. 39, no. 4, pp. 745–763, 1992.

[10] A. Karlin, M. Manasse, L. Rudolph, and D. Sleator, “Competitive SnoopyCaching,” Algorithmica, vol. 3, pp. 79–119, 1988.

[11] A. R. Karlin, M. S. Manasse, L. A. McGeoch, and S. Owicki, “Competitive RandomizedAlgorithms for Non-uniform Problems,” in Proceedings of ACM SODA.

[12] R. J. Brunner, S. G. Djorgovski, T. A. Prince, and A. S. Szalay, “Handbook of MassiveData Sets,” J. Abello, P. M. Pardalos, and M. G. C. Resende, Eds. Norwell,MA, USA: Kluwer Academic Publishers, 2002, ch. Massive atasets in Astronomy,pp. 931–979.

Cites this article as

[Pramod Kumar Sagar , Kanika Garg Assistant , Ruby Singh. Assistant (2015), Role of Cloud Computing in Cost Minimization: A Review, International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-2, Issue-3, Page No-56-59], (ISSN 2347 - 5552). www.ijirem.org