A high performance VoLTE traffic classification method using HTCondor

Voice-over-LTE (VoLTE) is a VoIP-based multimedia service which is provided using All-IP based LTE networks. VoLTE service was first commercialized by Korean telcos in 2012, and now more and more telcos are trying to adopt this technology. With the increased VoLTE service popularity, it is inevitable to have large VoLTE traffic volume (possibly degrading the service quality) and the potential attacks (possibly degrading the service reliability and availability) in the near future. Therefore, in order to avoid such potential issues, we need to perform thorough analysis on VoLTE traffic. As a first step, we propose a VoLTE traffic classification method and its distributed architecture. As the proposed classification method relies on Deep Packet Inspection (DPI) technique, it severely suffers from the large processing time and scalability issues. To overcome these issues, we further propose a distributed architecture for VoLTE traffic classification by adopting a high throughput computing framework - HTCondor. We performed a set of experiments using real-world traces captured from a commercial LTE core network, and have shown that with the proposed architecture, we can achieve up to 23.869 Gbps classification throughput which was almost 35 times faster than the system without distributed processing.

[1]  Grenville J. Armitage,et al.  Rapid identification of Skype traffic flows , 2009, NOSSDAV '09.

[2]  Maode Ma,et al.  A VoIP Traffic Identification Scheme Based on Host and Flow Behavior Analysis , 2010, Journal of Network and Systems Management.

[3]  L. Deri Improving Passive Packet Capture : Beyond Device Polling , 2003 .

[4]  Dario Rossi,et al.  Revealing skype traffic: when randomness plays with you , 2007, SIGCOMM '07.

[5]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[6]  Youngseok Lee,et al.  Toward scalable internet traffic measurement and analysis with Hadoop , 2013, CCRV.

[7]  Dario Rossi,et al.  Reviewing Traffic Classification , 2013, Data Traffic Monitoring and Analysis.

[8]  James Won-Ki Hong,et al.  Application‐Level Traffic Monitoring and an Analysis on IP Networks , 2005 .

[9]  James Won-Ki Hong,et al.  Fine‐grained traffic classification based on functional separation , 2013, Int. J. Netw. Manag..

[10]  Henning Schulzrinne,et al.  RTP: A Transport Protocol for Real-Time Applications , 1996, RFC.

[11]  Dario Rossi,et al.  Experiences of VoIP traffic monitoring in a commercial ISP , 2010, Int. J. Netw. Manag..

[12]  Alekh Jindal,et al.  Hadoop++ , 2010 .

[13]  PJ Radcliffe,et al.  VoIP traffic classification in IPSec tunnels , 2010, 2010 International Conference on Electronics and Information Engineering.

[14]  Christian Callegari,et al.  A Real-Time Algorithm for Skype Traffic Detection and Classification , 2009, NEW2AN.

[15]  Sukumar Nandi,et al.  A technique for classification of VoIP flows in UDP media streams using VoIP signalling traffic , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[16]  Aslam Khan,et al.  A Generic Technique for Voice over Internet Protocol (VoIP) Traffic Detection , 2008 .

[17]  Riyad Alshammari,et al.  An investigation on the identification of VoIP traffic: Case study on Gtalk and Skype , 2010, 2010 International Conference on Network and Service Management.