Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing

In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices. key words: software-defined networking (SDN), network functions virtualisation (NFV), network virtualization, 5G, network slicing

[1]  Carey L. Williamson,et al.  Identifying and discriminating between web and peer-to-peer traffic in the network core , 2007, WWW '07.

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

[3]  Oliver Spatscheck,et al.  Accurate, scalable in-network identification of p2p traffic using application signatures , 2004, WWW '04.

[4]  Sebastian Zander,et al.  Self-Learning IP Traffic Classification Based on Statistical Flow Characteristics , 2005, PAM.

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

[6]  Akihiro Nakao,et al.  VNode infrastucture enhancement — Deeply programmable network virtualization , 2015, 2015 21st Asia-Pacific Conference on Communications (APCC).

[7]  Andrew W. Moore,et al.  Bayesian Neural Networks for Internet Traffic Classification , 2007, IEEE Transactions on Neural Networks.

[8]  Tarik Taleb,et al.  End-to-end Network Slicing for 5G Mobile Networks , 2017, J. Inf. Process..

[9]  Akihiro Nakao,et al.  Application Specific Mobile Edge Computing through Network Softwarization , 2016, 2016 5th IEEE International Conference on Cloud Networking (Cloudnet).

[10]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[11]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[12]  Akihiro Nakao,et al.  Network Virtualization as Foundation for Enabling New Network Architectures and Applications , 2010, IEICE Trans. Commun..

[13]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[14]  Stuart Cheshire,et al.  Internet Assigned Numbers Authority (IANA) Procedures for the Management of the Service Name and Transport Protocol Port Number Registry , 2011, RFC.

[15]  Cormac J. Sreenan,et al.  mmdump: a tool for monitoring internet multimedia traffic , 2000, CCRV.

[16]  J. Erman,et al.  QRP05-4: Internet Traffic Identification using Machine Learning , 2006, IEEE Globecom 2006.