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.

In the context of the emergent Software-Defined Networking (SDN) paradigm, the attention is mostly directed to the evolution of control protocols and networking functionalities. However, network professionals also need the right tools to reach the same level—and beyond—of monitoring and control they have in traditional networks. Current SDN tools are developed on an ad hoc basis, for specific SDN frameworks, while production environments demand standard platforms and easy integration. This survey aims to foster the definition of the next generation SDN management framework by providing the readers a thorough overview of existing SDN tools and main research directions.

Telefónica's OnLife project aims to virtualize the access network and give third-party Internet of Things application developers and content providers cloud-computing capabilities at the network edge.

Currently, software-defined networking (SDN) platforms leverage the link-layer discovery protocol (LLDP) to discover the underlying topology. However, the LLDP is suboptimal in terms of message load. In this letter, we present the tree exploration discovery protocol (TEDP), proving that shortest paths can be built at the same time that the topology information is gathered, without extra messages compared with LLDP. We also analyze two alternative implementations for the TEDP and give insights into some features that SDN platforms should ideally provide for an efficient topology discovery service.