IARA: An Intelligent Application-Aware VNF for Network Resource Allocation with Deep Learning

Application awareness is essential for traffic engineering and Quality of Service (QoS) guarantee, especially in Internet of Things (IoT). Software Defined Network (SDN) with centralized controlling of network resources provides opportunities for fine- grained resource allocation. However, the controller cannot autonomously identify applications effectively, because sampling and recognizing traffic data consumes a lot of IO and computing resources. In this demonstration, we provide an intelligent application-aware Virtualized Network Function (VNF) with deep learning technology to identify the network traffic. The traffic type information will be mapped to specific network requirements and utilized to search appropriate route paths for different applications. The intelligent VNF is deployed on a GPU-equipped standalone server and works on the data plane of SDN. It identifies the traffic and sends the type information to the controller through OpenFlow protocol. The experiments show that by introducing the type information, SDN controller can assign more appropriate route paths for different types of traffic and highly improve the network QoS.

[1]  Wei Wu,et al.  Efficient and robust feature extraction and selection for traffic classification , 2017, Comput. Networks.

[2]  Ian F. Akyildiz,et al.  A roadmap for traffic engineering in SDN-OpenFlow networks , 2014, Comput. Networks.

[3]  Tal Mizrahi,et al.  The case for Data Plane Timestamping in SDN , 2016, 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[4]  Jaime Lloret,et al.  Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things , 2017, IEEE Access.

[5]  Kotaro Kataoka,et al.  AMPS: Application aware multipath flow routing using machine learning in SDN , 2017, 2017 Twenty-third National Conference on Communications (NCC).