Abstract
This paper is to improvising and optimizing the scenario of Big data processing in cloud computing. A homogeneous cluster setup supports static nature of processing which is a huge disadvantage for optimizing the response time towards clients. In order to avail utmost client satisfaction, the host server needs to be upgraded with the latest technology to fulfil all requirements. Big data processing is a common frequent event in today’s Internet and the proposed framework improvises the response time. This will also make sure that the user gets its entire requirement fulfilled in optimal time. In order to avail utmost client satisfaction, the server needs to eliminate homogeneous cluster setup that is encountered usually in parallel data processing. The homogeneous cluster setup is static in nature and dynamic allocation of resources is not possible in this kind of environment. This will improve the overall resource utilization and, consequently, reduce the processing cost.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wu, S., Li, F., Mehrotra, S., Ooi, B.C.: Query optimization for massively parallel data processing. School of Computing, National University of Singapore, March 2012
Parallel Data Processing. http://server-demo-ec2.cloveretl.com/clover/docs/clustering-parallel-processing.html
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In OSDI’04: Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation, pp. 10–10. Berkeley, CA, USA, 2004. USENIX Association
Chih Yang, H., Dasdan, A., Hsiao, R.-L., Parker, D.S.: Map-Reduce-Merge: simplified relational data processing on large clusters. In: SIGMOD’07: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 1029–1040. New York, NY, USA, 2007. ACM
Lee, K.H., Lee, Y.J.: Big data processing with Map Reduce: A Survey. Department of Computer Science KAIST, December 2011
Gropp, W., Lusk, E., Skjellum, A.: Using MPI: Portable Parallel Programming with the Message-Passing Interface. MIT Press, Cambridge, MA (1999)
Deelman, E., Singh, G., Su, M.-H., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Berriman, G.B., Good, J., Laity, A., Jacob, J.C., Katz, D.S.: Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci. Program. 13(3), 219–237 (2005)
Pike, R., Dorward, S., Griesemer, R., Quinlan, S.: Interpreting the data: parallel analysis with Sawzall. Sci. Program. 13(4), 277–298 (2005)
Thain, D., Tannenbaum, T., Livny, M.: Distributed computing in practice: the condor experience. Concurrency Comput.: Pract. Exp. (2004)
Pike, R., Dorward, S., Griesemer, R., Quinlan, S.: Interpreting the data: parallel analysis with Sawzall. Sci. Program. 13(4), 277–298 (2005)
Jiang, D., et al.: Map-join-reduce: towards scalable and efficient data analysis on large clusters. IEEE Trans. Knowl. Data Eng. (2010)
Li, B., et al.: A platform for scalable one-pass analytics using MapReduce. In: Proceedings of the 2011 ACM SIGMOD, 2011
Babu, S.: Towards automatic optimization of map reduce programs. In: Proceedings of the 1st ACM Symposium on Cloud Computing, pp. 137–142 (2010)
Arpaci-Dusseau, A.C., Arpaci-Dusseau, R.H., Culler, D.E., Hellerstein, J.M., Patterson, D.A.: High-performance sorting on networks of workstations. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, Tucson, Arizona, May 1997
Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wychoff, P., Murthy, R.: Hive—a warehousing solution over a map-reduce framework. In: VLDB, 2009
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Praveen Kumar, Rathore, V.S. (2016). Improvising and Optimizing Resource Utilization in Big Data Processing. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_28
Download citation
DOI: https://doi.org/10.1007/978-981-10-0448-3_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0447-6
Online ISBN: 978-981-10-0448-3
eBook Packages: EngineeringEngineering (R0)