As opposed to the decentralized control logic underpinning the devising of the Internet as a complex bundle of box-centric protocols and vertically-integrated solutions, the SDN paradigm advocates the separation of the control logic from hardware and its centralization in software-based controllers. These key tenets offer new opportunities to introduce innovative applications and incorporate automatic and adaptive control aspects, thereby easing network management and guaranteeing the user’s QoE. Despite the excitement, SDN adoption raises many challenges including the scalability and reliability issues of centralized designs that can be addressed with the physical decentralization of the control plane. However, such physically distributed, but logically centralized systems bring an additional set of challenges. This paper presents a survey on SDN with a special focus on the distributed SDN control. Besides reviewing the SDN concept and studying the SDN architecture as compared to the classical one, the main contribution of this survey is a detailed analysis of state-of-the-art distributed SDN controller platforms which assesses their advantages and drawbacks and classifies them in novel ways (physical and logical classifications) in order to provide useful guidelines for SDN research and deployment initiatives. A thorough discussion on the major challenges of distributed SDN control is also provided along with some insights into emerging and future trends in that area.

Exposure to contaminated water during aquatic recreational activities can lead to gastrointestinal diseases. In order to decrease the exposure risk, the fecal indicator bacteria Escherichia coli is routinely monitored, which is time-consuming, labor-intensive, and costly. To assist the stakeholders in the daily management of bathing sites, models have been developed to predict the microbiological quality. However, model performances are highly dependent on the quality of the input data which are usually scarce. In our study, we proposed a conceptual framework for optimizing the selection of the most adapted model, and to enrich the training dataset. This frameword was successfully applied to the prediction of Escherichia coli concentrations in the Marne River (Paris Area, France). We compared the performance of six machine learning (ML)-based models: K-nearest neighbors, Decision Tree, Support Vector Machines, Bagging, Random Forest, and Adaptive boosting. Based on several statistical metrics, the Random Forest model presented the best accuracy compared to the other models. However, 53.2 ± 3.5% of the predicted E. coli densities were inaccurately estimated according to the mean absolute percentage error (MAPE). Four parameters (temperature, conductivity, 24 h cumulative rainfall of the previous day the sampling, and the river flow) were identified as key variables to be monitored for optimization of the ML model. The set of values to be optimized will feed an alert system for monitoring the microbiological quality of the water through combined strategy of in situ manual sampling and the deployment of a network of sensors. Based on these results, we propose a guideline for ML model selection and sampling optimization.

Software‐Defined Networking (SDN) brought a groundbreaking idea to facilitate network system management by decoupling and abstracting the Control plane and Data plane of traditional networks. The centralised control offers network administrators many benefits such as a global view of the network, programmability, dynamic updating of forwarding rules, and software‐based traffic analysis. The SDN architecture has been applied a lot in practice, and especially in Internet of Things (IoT) platforms. With the superiority of SDN, IoT devices can be managed and configured much more easily when combined. However, SDN also raises many challenges in terms of scalability, reliability, and security. Blockchain is another promising solution for secure information storage and transmission technology that operates without a centralised authority. Applying Blockchain technology into SDN can address some of the current issues of SDN by providing decentralised methods to authenticate exchanged network information. This study provides a comprehensive survey on Blockchain technologies applied to SDN in both security and non‐security fields. First, related studies and an overview of SDN and the background of Blockchain technology are presented. Then, the authors review how Blockchain technologies are applied in SDN from two perspectives: non‐security and security‐aware approaches. Finally, challenges and broader perspectives are discussed.

In the years since its initiation, the software-defined network paradigm has evolved into a distinguished networking technology by causing a revolution in separating the control logic from physical devices and centralizing software-based controllers. Despite its indisputable benefits compared with the traditional network, the SDN raises the challenge of scalability with its physically centralized control. The only potential solution is to transform it into physically distributed SDN control. However, this solution requires the interoperability between SDN controllers, and the consistency of network state being distributed across these controllers. Although some east–west interfaces that help SDN controllers exchange network information have been released, they reveal several drawbacks. First, they cannot support a heterogeneous SDN system where SDN controllers are developed by different providers. Secondly, their consistency solution is simple in disregarding the tradeoff between the consistency level and the performance of SDN networks. This paper proposes an east–west interface, called SINA, to provide the interoperability of a heterogeneous and distributed SDN network. In addition, a novel reinforcement-learning-based consistency algorithm is introduced for an adaptive, quorum-based replication mechanism. The experimental results showed that SINA successfully connects heterogeneous and distributed SDN domains and balances the consistency and network performance.

With the rapid growth of fifth-generation (5G), network operators have met with difficulties in ensuring user satisfaction due to diverse service-level agreement (SLA) requirements. Application-aware routing is able to potentially address this issue in implementing differentiated routing policies corresponding to various applications. To facilitate the application-aware routing, software-defined networking (SDN) and segment routing (SR) are potential solutions with programmability property to steer network traffic into appropriate routing paths. However, application-aware SR encounters two main problems, including application identification for encrypted traffic and path identification with human intervention. Therefore, this article proposes a new SDN-based SR mechanism that could help network operators overcome these problems. This approach implements reinforcement learning to adapt to dynamic networks in order to optimize the quality of experience (QoE) that network operators must guarantee. The proposal also considers the application class to implement corresponding routing policies for various applications to meet the stringent SLA requirements. Identifying the applications is sometimes complicated due to encrypted network traffic. Hence, our proposal implements a traffic classification approach to classify encrypted traffic into different kinds of applications. Obtained results under considered conditions illustrate that our proposal outperforms the standard SR mechanism related to QoE and decreases up to 64.39% of overhead compared to several benchmarks.

Software-Defined Networking (SDN) and its extension Intent-Based Networking (IBN) are network paradigms that enable dynamic, programmatically efficient network configuration. IBN allows network operators to express an outcome or business objective without the low-level configurations necessary to program the network to achieve these demands. Existing research proposals for IBN introduce several systems to translate users intents into network infrastructure configurations. Despite the positive aspects of these proposals, they still suffer from many drawbacks. Some require users to learn a new intent definition language. Some others may lack the appropriate grammar to make these frameworks recognize the intent correctly. In this paper, we introduce a framework leveraging the capabilities of Natural Language Processing (NLP) for network management from an operator utterances. In order to understand natural language, our framework uses the sequence-to-sequence (seq2seq) learning model based on recurrent neural networks (LSTM). The model has been improved by using word embedding and user feedback. As a proof of concept, we implement our framework for network management in a Global Internet eXchange Network and evaluate its practicality regarding NLP accuracy and network performance.