A Survey on Machine Learning and Deep Learning based Quality of Service
aware Protocols for Software Defined Networks
Abstract
Quality of Service (QoS) is one of the most important parameters to be
considered in computer networking and communication. The traditional
network incorporates various quality QoS frameworks to enhance the
quality of services. Due to the distributed nature of the traditional
networks, providing quality of service, based on service level agreement
(SLA) is a complex task for the network designers and administrators.
With the advent of software defined networks (SDN), the task of ensuring
QoS is expected to become feasible. Since SDN has logically centralized
architecture, it may be able to provide QoS, which was otherwise
extremely difficult in traditional network architectures. Emergence and
popularity of machine learning (ML) and deep learning (DL) have opened
up even more possibilities in the line of QoS assurance. In this
article, the focus has been mainly on machine learning and deep learning
based QoS aware protocols that have been developed so far for SDN. The
functional areas of SDN namely traffic classification, QoS aware
routing, queuing, and scheduling are considered in this survey. The
article presents a systematic and comprehensive study on different ML
and DL based approaches designed to improve overall QoS in SDN.
Different research issues & challenges, and future research directions
in the area of QoS in SDN are outlined.