Solving a Big-Data Problem with GPU: The Network Traffic Analysis

The number of devices connected to the Internet has increased significantly and will grow exponentially in the near future, it is due to the lower costs. It is expected that next years, data traffic via Internet increases up to values around zettabyte. As a consequence of this increase, it can be observed that the data traffic is growing faster than the capacity of their processing. In recent years, the identification of Internet traf- fic generated by different applications has become one of the major challenges for telecommunications networks. This characterization is based on understanding the composition and dynamics of Internet traffic to improve network performance. To analyse a huge amount of data generated by networks traffic in real time requires more power and capacity computing. A good option is to apply High Performance Computing techniques in this problem, especifically use Graphics Processing Unit (GPU). Its main characteristics are high computational power, constant development and low cost, besides provides a kit of programming called CUDA. It offers a GPUCPU interface, thread synchronization, data types, among others. In this paper we present the causes of increasing data volumes circulating on the network, data analysis and monitoring current techniques, and the feasibility of combining data mining techniques with GPU to solve this problem and speed up turnaround times.

[1]  Inyoung Kim,et al.  A latent class modeling approach to detect network intrusion , 2006, Comput. Commun..

[2]  Yun Wang,et al.  Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection , 2008 .

[3]  Nicholas Wilt,et al.  The CUDA Handbook: A Comprehensive Guide to GPU Programming , 2013 .

[4]  Djamel Fawzi Hadj Sadok,et al.  Multi-gigabit traffic identification on GPU , 2013, HPPN '13.

[5]  Walter Didimo,et al.  Graph visualization techniques for conceptual Web site traffic analysis , 2010, 2010 IEEE Pacific Visualization Symposium (PacificVis).

[6]  Le Gruenwald,et al.  High-Performance Spatial Query Processing on Big Taxi Trip Data Using GPGPUs , 2014, 2014 IEEE International Congress on Big Data.

[7]  Jie Cheng,et al.  Programming Massively Parallel Processors. A Hands-on Approach , 2010, Scalable Comput. Pract. Exp..

[8]  Antonio Pescapè,et al.  Issues and future directions in traffic classification , 2012, IEEE Network.

[9]  J. P. Ed,et al.  Transmission control protocol- darpa internet program protocol specification , 1981 .

[10]  Sushil Jajodia,et al.  Detecting Novel Network Intrusions Using Bayes Estimators , 2001, SDM.

[11]  Salvatore J. Stolfo,et al.  A data mining framework for building intrusion detection models , 1999, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344).

[12]  Judith Kelner,et al.  Deep packet inspection tools and techniques in commodity platforms: Challenges and trends , 2012, J. Netw. Comput. Appl..

[13]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[14]  David Loshin,et al.  Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph , 2013 .

[15]  V. Mayer-Schönberger.  [Big data: a revolution that will transform our lives]. , 2015, Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz.

[16]  Stenio F. L. Fernandes,et al.  GPU-oriented stream data mining traffic classification , 2014, 2014 IEEE Symposium on Computers and Communications (ISCC).

[17]  Mark Sullivan,et al.  Tribeca: A Stream Database Manager for Network Traffic Analysis , 1996, VLDB.

[18]  Konstantina Papagiannaki,et al.  Structural analysis of network traffic flows , 2004, SIGMETRICS '04/Performance '04.

[19]  Jeffrey G. Glosup.  Statistical Methods in Computer Security , 2006, Technometrics.

[20]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[21]  Carolyn L. Talcott,et al.  Analyzing BGP Instances in Maude , 2011, FMOODS/FORTE.

[22]  Wenji Wu,et al.  G-NetMon: A GPU-accelerated network performance monitoring system for large scale scientific collaborations , 2011, 2011 IEEE 36th Conference on Local Computer Networks.

[23]  John D. Owens,et al.  GPU Computing , 2008, Proceedings of the IEEE.

[24]  Ettikan Kandasamy Karuppiah,et al.  A Multi-GPU Framework for In-Memory Text Data Analytics , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[25]  Judith Kelner,et al.  A Survey on Internet Traffic Identification , 2009, IEEE Communications Surveys & Tutorials.

[26]  Marc Suñé Clos.  A framework for network traffic analysis using GPUs , 2010 .