Hadoop Based Big Data Traffic Handling in Ample Cellular Network

International Journal of Mobile Computing and Application

© 2014 by SSRG - IJMCA Journal

Volume 1 Issue 2

Year of Publication : 2014

Authors : K.Aishwarya , Ms.B.M.Brinda and Ms.K.B.SriSathya

10.14445/23939141/IJMCA-V1I3P104

How to Cite?

K.Aishwarya , Ms.B.M.Brinda and Ms.K.B.SriSathya, "Hadoop Based Big Data Traffic Handling in Ample Cellular Network," SSRG International Journal of Mobile Computing and Application, vol. 1,  no. 2, pp. 13-15, 2014. Crossref, https://doi.org/10.14445/23939141/IJMCA-V1I3P104

Abstract:

Big Data creates major problems with large-volume of traffic, growing data access, complex data sets with numerous, autonomous sources. With the quick development of networking, data storage, and also the data assortment capability, big data is currently increasing rapidly in science and engineering domains, as well as physical, biological and bio-medical sciences. The size of those data will become impractical. Hence, in such cases, the analyst should be capable of specializing within the informational data while ignoring the noise data. These difficulties complicate the multichannel data analysis when compared with the analysis of singlechannel data. Traffic management in internet is difficult because a large data set requires matching computing and storage resources. The proposed work provides security for traffic data in ample cellular network and big data traffic handling based on hadoop. Due to handling of large data in efficient manner, the proposed work provides model process high traffic data with high performance.

Keywords:

Big Data, Hadoop, Map Reduce, Traffic handling.

References:

[1] S. Ghemawat, H. Gobioff , and S. T. Leung, “The Google File System,” ACM SIGOPS Operating Systems Rev., vol. 37, no. 5, 2003.
[2] J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” Commun. ACM, vol. 51, no. 1, 2008, pp. 107–13.
[3] Hadoop, http://hadoop.apache.org/.
[4] T. White, Hadoop: the Definitive Guide, O’Reilly, 3rd ed., 2012.
[5] Large-Scale PCAP Data Analysis Using Apache Hadoop,https://labs.ripe.net/Members/wnagele/large-scale-pcapdata-analysis-using-apache-hadoop, accessed 9 Nov., 2013.
[6] T. Samak, D. Gunter, and V. Hendrix, “Scalable Analysis of Network Measurements with Hadoop and Pig,” IEEE Network Operations and Management Symp., 2012, pp. 1254–59.
[7] T. P. D. B. Vieira, S. F. D. L Fernandes, V. C. Garcia, “Evaluating MapReduce for profiling Application Traffic,” Proc. 1st ACM Wksp. High Performance and Programmable Networking, 2013, pp. 45–52. 
[8] Tcpdump, http://www.tcpdump.org.
[9] B. B gupta. , Joshi, R. C. and Misra, Manoj(2009), 'Defending against Distributed Denial of Service Attacks: Issues and Challenges', Information Security Journal: A Global Perspective, 18: 5, 224 — 247. 
[10] J. T. Morken, Distributed netow processing using the map-reduce model," Ph.D. dissertation, Norwegian University of Science and Technology, 2010.
[11] Y. Lee and Y. Lee, “Toward Scalable Internet Traffic Measurement and Analysis with Hadoop,” ACM SIGCOMM Comp. Commun. Rev., vol. 43, no. 1, 2012, pp. 5–13.
[12] Large-Scale PCAP Data Analysis Using Apache Hadoop,https://labs.ripe.net/Members/wnagele/large-scale-pcapdata-analysis-using-apache hadoop, accessed 9 Nov., 2013. 
[13] T. P. D. B. Vieira, S. F. D. L Fernandes, V. C. Garcia, “Evaluating MapReduce for profiling Application Traffic,” Proc. 1st ACM Wksp. High Performance and Programmable Networking, 2013, pp. 45–52.
[14] F. Ricca et al., “An Empirical Study on Keyword-Based Web Site Clustering,” Proc. 12th IEEE Int”l. Wksp. Program Comprehension, 2004, pp. 204–13.
[15] The R Project for Statistical Computing, http://www.r-project.org/, accessed 9 Nov., 2013.
[16] J. Liu and N. Ansari, “Identifying Website Communities in Mobile Internet Based on Affinity Measurement,” Computer Commun., vol. 41, 2014, pp. 22–20.
[17] G. Chittaranjan, J. Blom, and D. Gatica-Perez, “Who’s Who with Big- Five: Analyzing and Classifying Personality Traits with Smartphones,” IEEE 15th Annual Int’l. Symp. Wearable Computers, 2011, pp. 29–36.
[18] D. Tanasa D and B. Trousse, “Advanced Data Preprocessing for Intersites Web Usage Mining,” IEEE Intelligent Systems, vol. 19, no. 2, 2004, pp. 59–65.
[19] Wireless Universal Resource FiLe Open Source Project (WURFL), http://wurfl.sourceforge.net/, [Online; accessed 9-Nov.-2013].