Advanced SearchSearch Tips
Big-Data Traffic Analysis for the Campus Network Resource Efficiency
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Big-Data Traffic Analysis for the Campus Network Resource Efficiency
An, Hyun-Min; Lee, Su-Kang; Sim, Kyu-Seok; Kim, Ik-Han; Jin, Seo-Hoon; Kim, Myung-Sup;
  PDF(new window)
The importance of efficient enterprise network management has been emphasized continuously because of the rapid utilization of Internet in a limited resource environment. For the efficient network management, the management policy that reflects the characteristics of a specific network extracted from long-term traffic analysis is essential. However, the long-term traffic data could not be handled in the past and there was only simple analysis with the shot-term traffic data. However, as the big data analytics platforms are developed, the long-term traffic data can be analyzed easily. Recently, enterprise network resource efficiency through the long-term traffic analysis is required. In this paper, we propose the methods of collecting, storing and managing the long-term enterprise traffic data. We define several classification categories, and propose a novel network resource efficiency through the multidirectional statistical analysis of classified long-term traffic. The proposed method adopted to the campus network for the evaluation. The analysis results shows that, for the efficient enterprise network management, the QoS policy must be adopted in different rules that is tuned by time, space, and the purpose.
Enterprise network;Big data traffic;Statistical analysis;Long-term traffic;Network policy;
 Cited by
Y. Wang, Y. Xiang, W. L. Zhou, and S. Z. Yu, "Generating regular expression signatures for network traffic classification in trusted network management," J. Network Comput. Appl., vol. 35, pp. 992-1000, May 2012. crossref(new window)

B. Park, Y. Won, J. Chung, M. S. Kim, and J. W. K. Hong, "Fine-grained traffic classification based on functional separation," Int. J. Network Management, vol. 23, pp. 350-381, Sept. 2013. crossref(new window)

C. S. Park, J. S. Park, and M. S. Kim, "Automatic Payload Signature Generation System," J. KICS, vol. 38B, no. 08, pp. 615-622, Aug. 2013. crossref(new window)

J. H. Choi, J. S. Park, and M. S. Kim, "Processing speed improvement of HTTP traffic classification based on hierarchical structure of signature," J. KICS, vol. 39B, no. 04, pp. 191-199, Apr. 2014. crossref(new window)

J. S. Park, S. H. Yoon, and M. S. Kim, "Performance improvement of the payload signature based traffic classification system using application traffic locality," J. KICS, vol. 38B, no. 7, pp. 519-525, Jul. 2013. crossref(new window)

S. Lohr, The age of big data, New York Times, 11, 2012.

T. Oetiker, "Monitoring your IT gear: the MRTG story," IT Professional, vol. 3, no. 6, pp. 44-48, 2001.

RRDtool, Available at:

Bro, Available:

Ntop, Available:

Snort, Available at:

B. H. Hong and H. J. Joo, "A study on the monitoring model for traffic analysis and application of big data," 2013.

S. P. Huang and G. E. Meng, "Research on the application of hadoop platform in the big data processing," Modern Computer, vol. 29, no. 4, 2013.

Hadoop, Available:

A. D. Sarma, F. N. Afrati, S. Salihoglu, and J. D. Ullman, "Upper and lower bounds on the cost of a map-reduce computation," Very Large Data Bases(VLDB) Endownment, pp. 277-288, Riva del Garda, Italy, 2013.