There is increasing demand in modern day business applications for communication networks to be robust and reliable due to the complexity and critical nature of such applications. As such, data delivery is expected to be reliable and secure even in the harshest of environments. Software-Defined Networking (SDN) is gaining traction as a promising approach for designing network architectures which are robust and flexible. One reason for this is that separating the data plane from the control plane, increases the controller’s ability to configure the network rapidly. When network failure events occur, the network manager may trade-off the optimality of the achieved network reconfiguration with the responsivenss of the reconfiguration process. Responsiveness may be favoured when the network resources are under stress and the failure rate is high. We contribute SDN recovery methods that leverage information about the structure of the network to expedite network restoration when a link failure occurs. They operate by detecting community-like structures in the network topology and then they find alternative paths which have low operation and installation costs using this information. Extensive simulations are conducted to evaluate the proposed SDN recovery methods using open-source simulation tools. They provide evidence that the proposed approaches lead to performance gains when an alternative path is required among a set of candidate paths.

In this paper, we introduce a new approach that computes the shortest-reliable end-to-end paths for centrally controlled networks like software-defined networks (SDNs). The proposed method aims to find the correlation between the routing mechanism and reliability with the purpose of decreasing the required time of backup path installation through reducing the number of required rules at the moment of failure towards guarantee the fast restoration of the affected path, hence leading to the reduction of the overhead on SDN network controller and the probability of the loss of packets. We also investigate the correlation between the network topology and its reliability and demonstrate the benefits from this relation through experiments using well known SDN network simulation tools.

In recent years, the emerging paradigm of software-defined networking has become a hot and thriving topic that grabbed the attention of industry sector as well as the academic research community. The decoupling between the network control and data planes means that software-defined networking architecture is programmable, adjustable and dynamically re-configurable. As a result, a large number of leading companies across the world have latterly launched software-defined solutions in their data centers and it is expected that most of the service providers will do so in the near future due to the new opportunities enabled by software-defined architectures. Nonetheless, each emerging technology is accompanied by new issues and concerns, and fault tolerance and recovery is one such issue that faces software-defined networking. Although there have been numerous studies that have discussed this issue, gaps still exist and need to be highlighted. In this paper, we start by tracing the evolution of networking systems from the mid 1990's until the emergence of programmable networks and software-defined networking, and then define a taxonomy for software-defined networking dependability by means of fault tolerance of data plane to cover all aspects, challenges and factors that need to be considered in future solutions. We discuss in a detailed manner current state-of-the-art literature in this area. Finally, we analyse the current gaps in current research and propose possible directions for future work.

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Software Defined Networking is a new networking paradigm that has emerged recently as a promising solution for tackling the inflexibility of the classical IP networks. The centralized approach of SDN yields a broad area for intelligence to optimise the network at various levels. Fault tolerance is considered one of the most current research challenges that facing the SDN, hence, in this paper we introduce a new method that computes an alternative paths reactively for centrally controlled networks like SDN. The proposed method aims to reduce the update operation cost that the SDN network controller would spend in order to recover from a single link failure. Through utilising the principle of community detection, we define a new network model for the sake of improving the network's fault tolerance capability. An experimental study is reported showing the performance of the proposed method. Based on the results, some further directions are suggested in the context of machine learning towards achieving further advances in this research area.

Software-defined networking offers numerous benefits against the legacy networking systems through simplifying the process of network management and reducing the cost of network configuration. Currently, the management of failures in the data plane is limited to two mechanisms: proactive and reactive. Such failure recovery techniques are activated after occurrences of failures. Therefore, packet loss is highly likely to occur as a result of service disruption and unavailability. This issue is not only related to the slow speed of recovery mechanisms, but also the delay caused by the failure detection process. In this paper, we define a new approach to the management of fault tolerance in software-defined networks where the goal is to eliminate the convergence process altogether, rather than speed up failure detection and recovery. We propose a new framework, called Smart Routing, which works based on the forewarning signs on failures in order to compute alternative paths and isolate the risky links from the routing tables of the data plane devices. We validate our framework through a set of experiments that demonstrate how the underlying model runs.

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Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control plane from the data plane to result in a centralised network controller that maintains a global view over the whole network on its domain. In this paper, we propose a new deep learning model for software-defined networks that can accurately identify a wide range of traffic applications in a short time, called Deep-SDN. The performance of the proposed model was compared against the state-of-the-art and better results were reported in terms of accuracy, precision, recall, and f-measure. It has been found that 96% as an overall accuracy can be achieved with the proposed model. Based on the obtained results, some further directions are suggested towards achieving further advances in this research area.