Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 170723, 10 pages.

REST-MapReduce: An Integrated Interface but Differentiated Service

Jong-Hyuk Park, Hwa-Young Jeong, Young-Sik Jeong, and Min Choi

Full-text: Open access

Abstract

With the fast deployment of cloud computing, MapReduce architectures are becoming the major technologies for mobile cloud computing. The concept of MapReduce was first introduced as a novel programming model and implementation for a large set of computing devices. In this research, we propose a novel concept of REST-MapReduce, enabling users to use only the REST interface without using the MapReduce architecture. This approach provides a higher level of abstraction by integration of the two types of access interface, REST API and MapReduce. The motivation of this research stems from the slower response time for accessing simple RDBMS on Hadoop than direct access to RDMBS. This is because there is overhead to job scheduling, initiating, starting, tracking, and management during MapReduce-based parallel execution. Therefore, we provide a good performance for REST Open API service and for MapReduce, respectively. This is very useful for constructing REST Open API services on Hadoop hosting services, for example, Amazon AWS (Macdonald, 2005) or IBM Smart Cloud. For evaluating performance of our REST-MapReduce framework, we conducted experiments with Jersey REST web server and Hadoop. Experimental result shows that our approach outperforms conventional approaches.

Article information

Source
J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 170723, 10 pages.

Dates
First available in Project Euclid: 1 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1412176985

Digital Object Identifier
doi:10.1155/2014/170723

Citation

Park, Jong-Hyuk; Jeong, Hwa-Young; Jeong, Young-Sik; Choi, Min. REST-MapReduce: An Integrated Interface but Differentiated Service. J. Appl. Math. 2014, Special Issue (2014), Article ID 170723, 10 pages. doi:10.1155/2014/170723. https://projecteuclid.org/euclid.jam/1412176985


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References

  • J. Dean and S. Ghemawat, “MapReduce: simplied data processing on large clusters,” in USENIX International Conference on OSDI, 2004.
  • A. C. Murthy, C. Douglas, M. Konar et al., “Architecture of next generation apache hadoop MapReduce framework,” Tech. Rep., 2013.
  • “Processing and Loading Data from Amazon S3 to the Vertica Analytic Database,” White Paper, Amazon Web Service, 2013.
  • Hadoop, http://hadoop.apache.org/.
  • Amazon Elastic MapReduce Developer Guide, Amazon Web Service, 2009.
  • Getting Started with Amazon Elastic MapReduce, Amazon Web Service, March 2009.
  • I. Macdonald, “Ruby/Amazon & Amazon web services,” Dr. Dobb\textquotesingle s Journal, vol. 30, no. 2, pp. 30–34, 2005.
  • R. Hussain and H. Oh, “Cooperation-aware VANET clouds: providing secure cloud services to vehicular ad hoc networks,” Journal of Information Processing Systems, vol. 10, no. 1, pp. 103–118, 2014.
  • S. Islam, R. Rahman, A. Roy, I. Islam, and M. R. Amin, “Performance evaluation of finite queue switching under two-dimensional M/G/1(m) traffic,” Journal of Information Processing Systems, vol. 7, no. 4, pp. 679–690, 2011.
  • R. Pan, G. Xu, B. Fu, P. Dolog, Z. Wang, and M. Leginus, “Improving recommendations by the clustering of tag neighbours,” Journal of Convergence, vol. 3, no. 1, pp. 13–20, 2012.
  • H. Zhao and P. Doshi, “Towards automated RESTful Web service composition,” in Proceedings of the IEEE International Conference on Web Services (ICWS '09), pp. 189–196, July 2009.
  • X. Zhao, E. Liu, G. J. Clapworthy, N. Ye, and Y. Lu, “RESTful web service composition: extracting a process model from linear logic theorem proving,” in Proceedings of the 7th International Conference on Next Generation Web Services Practices (NWeSP '11), pp. 398–403, October 2011.
  • Z. Li and L. O'Brien, “Towards effort estimation for web service compositions using classification matrix,” 2010.
  • C. Pautasso, O. Zimmermann, and F. Leymann, “RESTful web services vs. “Big” web services: making the right architectural decision,” in Proceedings of the 17th International Conference on World Wide Web (WWW \textquotesingle 08), pp. 805–814, April 2008.
  • R. Alarcon, E. Wilde, and J. Bellido, “Hypermedia-driven RESTful service composition,” Service-Oriented Computing, Springer, vol. 6568, pp. 111–120, 2011.
  • M. Yoon, Y. K. Kim, and J. W. Jang, “An energy-efficient routing protocol using message success rate in wireless sensor networks,” Journal of Convergence, vol. 4, no. 1, 2013.
  • “The Internet of Things: In action, The Next Web,”http:// thenextweb.com/insider/2013/05/19/the-internet-of-things-in- action/.
  • J. Rao and X. Su, “A survey of automated Web service composition methods,” in Proceedings of the 1st International Workshop on Semantic Web Services and Web Process Composition (SWSWPC '04), pp. 43–54, July 2004.
  • J. Dean and S. Ghemawa, “MapReduce: simplified data processing on large clusters,” in Proceedings of the 6th USENIX Symposium on Operating System Design and Implementation, 2004.
  • C. Pautasso, “RESTful web service composition with BPEL for REST,” Data and Knowledge Engineering, vol. 68, no. 9, pp. 851–866, 2009.
  • F. O. Catak and M. E. Balaban, “CloudSVM: training an SVM classifier in cloud computing systems,” in Pervasive Computing and the Networked World, pp. 57–68, 2013.
  • IBM Smart Cloud, http://www.ibm.com/cloud-computing/ us/en/.
  • M. de Kruijf and K. Sankaralingam, MapReduce for the Cell B.E. Architecture, Vertical Research Group. Department of Computer Sciences, University of Wisconsin-Madison, 2010.
  • M. Choi, J. Park, and Y.-S. Jeong, “Mobile cloud computing framework for a pervasive and ubiquitous environment,” The Journal of Supercomputing, vol. 64, no. 2, pp. 331–356, 2013. \endinput