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

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.