Towards Smart Networking through Context Aware Traffic Identification Kit (TriCK) in 5G

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Title: Towards Smart Networking through Context Aware Traffic Identification Kit (TriCK) in 5G
Authors: Xu, Lina
Ruifeng, Duan
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Date: 12-Nov-2018
Online since: 2019-05-15T10:49:30Z
Abstract: In order to distribute diverse traffic flow into proper network interfaces, Access Network Discovery and Selection Function (ANDSF) is proposed by 3GPP, which can distribute every traffic flow to a preferred network interface according to several observed features from that flow. However, the static policies in ANDSF can neither understand the context nor adapt to real time changes. In order to address that problem, in our previous work, we have proposed a server-client based Context aware Traffic identification Kit (TriCK) to dynamically identify traffic, which can extend the functionalities of 3GPP ANDSF. It can classify traffic data not only based on its own characteristics, but also the real time network conditions and the current context. In this paper, we provide an implementation for the network selection component in TriCK based on clustering techniques, with a complexity of O(n). A static version and a dynamic version of the implementation are analysed. The static approach is easy to implement and comprehend. The static solution can distribute the traffic flow according to the traffic characteristics and the network context. The dynamic approach can further balance the traffic load between different network interfaces and therefore provide an overall better transmission quality.
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2018 IEEE
Keywords: Network interfacesServersWireless fidelityStreaming mediaLong Term EvolutionPerformance evaluation3GPP
DOI: 10.1109/ISNCC.2018.8531033
Language: en
Status of Item: Peer reviewed
Conference Details: The 2018 International Symposium on Networks, Computers and Communications (ISNCC-2018), Rome, Italy, 19-21 2018
ISBN: 978-1-5386-3779-1
Appears in Collections:Computer Science Research Collection

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