Towards Application-Aware Networking: ML-Based End-to-End Application KPI/QoE Metrics Characterization in SDN

Title: Towards Application-Aware Networking: ML-Based End-to-End Application KPI/QoE Metrics Characterization in SDN
Authors: Jahromi, Hamed Z.
Hines, Andrew
Delanev, Declan T.
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Date: 16-Aug-2018
Online since: 2019-05-15T11:48:27Z
Abstract: Software Defined Networking (SDN) presents a unique networking paradigm that facilitates the development of network innovations. This paper aims to improve application awareness by incorporating Machine Learning (ML) techniques within an open source SDN architecture. The paper explores how end-to-end application Key Performance Indicator (KPI) metrics can be designed and utilized for the purpose of application awareness in networks. The main goal of this research is to characterize application KPI metrics using a suitable ML approach based on available network data. Resource allocation and network orchestration tasks can be automated based on the findings. A key facet of this research is introducing a novel feedback interface to the SDN's Northbound Interface that receives realtime performance feedback from applications. This paper aim to show how could we exploit the applications feedback to determine useful characteristics of an application's traffic. A mapping application with a defined KPI is used for experimentation. Linear multiple regression is used to derive a characteristic relationship between the application KPI and the network metrics.
Type of material: Conference Publication
Publisher: IEEE
Start page: 126
End page: 131
Copyright (published version): 2018 IEEE
Keywords: MeasurementQuality of experienceServersQuality of serviceRandom access memoryComputer architectureProtocolsApplication AwarenessSDNKPIQoE
DOI: 10.1109/icufn.2018.8436625
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Language: en
Status of Item: Peer reviewed
Is part of: 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)
Conference Details: The 10th International Conference on Ubiquitous and Future Networks (ICUFN 2018), Prague, Czech Republic, 2-5 July 2018
ISBN: 978-1-5386-4646-5
Appears in Collections:Computer Science Research Collection

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