Abstract
In conventional networks, there was a tight bond between the control plane and the data plane. The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency, efficiency). SDN is a flexible networking paradigm that boosts network control, programmability and automation. It proffers many benefits in many areas, including routing. More specifically, for efficiently organizing, managing and optimizing routing in networks, some intelligence is required, and SDN offers the possibility to easily integrate it. To this purpose, many researchers implemented different machine learning (ML) techniques to enhance SDN routing applications. This article surveys the use of ML techniques for routing optimization in SDN based on three core categories (i.e. supervised learning, unsupervised learning, and reinforcement learning). The main contributions of this survey are threefold. Firstly, it presents detailed summary tables related to these studies and their comparison is also discussed, including a summary of the best works according to our analysis. Secondly, it summarizes the main findings, best works and missing aspects, and it includes a quick guideline to choose the best ML technique in this field (based on available resources and objectives). Finally, it provides specific future research directions divided into six sections to conclude the survey. Our conclusion is that there is a huge trend to use intelligence-based routing in programmable networks, particularly during the last three years, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on open datasets and topologies, and detailed practical implementations (following recent standards) that could be adopted by industry. In summary, future efforts should be focused on reproducible research rather than on new isolated ideas. Otherwise, most of these applications will be barely implemented in practice.
Highlights
U NTIL few years ago, most company networks followed a traditional approach
The search of the state of the art was mainly performed using the Google Scholar site, which comprehensively indexes works from different journals and sites, and even from archive repositories. They main keywords used were: routing, Software-Defined Networking (SDN) and machine learning (ML), which are the three core terms in relation with the survey, but we looked for Artificial Intelligence (AI), optimization, traffic engineering, load balancing, Network Functions Virtualisation (NFV), learning, supervised, unsupervised and reinforcement, among others
The results obtained show that even in complex networks, the proposed approach can significantly improve the performance of the routing configurations
Summary
U NTIL few years ago, most company networks followed a traditional approach. In particular, legacy networking devices obeyed an architecture based on a tight bond between control and data planes [1], translated into a vendor lock-in, in which networks became complex and difficult to maintain and manage, as they rapidly grew. While other surveys have a more generalist approach (focusing either on SDN or ML, different networking applications, and providing an overall idea), our survey aims to delve into specific routing applications and why ML has become such an important actor thanks to SDN (i.e., centralizing the logic and facilitating the integration of ML, otherwise unfeasible in traditional routing approaches, mostly distributed) It analyzes and compares all works, including the techniques leveraged, their specific objective (considering all of them are focused on routing), their implementation and evaluation, pros and cons This analysis is concluded by a summary of learned lessons and research trends. It provides a comprehensive section including future research directions, which, from our point of view, represents the most interesting part of the survey, as much work still needs to be done in the field to be relevant in a long-term manner
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