Sdn Software Defined Networks Epub To 21

Abstract:With the increased number of Software-Defined Networking (SDN) installations, the data centers of large service providers are becoming more and more agile in terms of network performance efficiency and flexibility. While SDN is an active and obvious trend in a modern data center design, the implications and possibilities it carries for effective and efficient network management are not yet fully explored and utilized. With most of the modern Internet traffic consisting of multimedia services and media-rich content sharing, the quality of multimedia communications is at the center of attention of many companies and research groups. Since SDN-enabled switches have an inherent feature of monitoring the flow statistics in terms of packets and bytes transmitted/lost, these devices can be utilized to monitor the essential statistics of the multimedia communications, allowing the provider to act in case of network failing to deliver the required service quality. The internal packet processing in the SDN switch enables the SDN controller to fetch the statistical information of the particular packet flow using the PacketIn and Multipart messages. This information, if preprocessed properly, can be used to estimate higher layer interpretation of the link quality and thus allowing to relate the provided quality of service (QoS) to the quality of user experience (QoE). This article discusses the experimental setup that can be used to estimate the quality of speech communication based on the information provided by the SDN controller. To achieve higher accuracy of the result, latency characteristics are added based on the exploiting of the dummy packet injection into the packet stream and/or RTCP packet analysis. The results of the experiment show that this innovative approach calculates the statistics of each individual RTP stream, and thus, we obtain a method for dynamic measurement of speech quality, where when quality decreases, it is possible to respond quickly by changing routing at the network level for each individual call. To improve the quality of call measurements, a Convolutional Neural Network (CNN) was also implemented. This model is based on two standard approaches to measuring the speech quality: PESQ and E-model. However, unlike PESQ/POLQA, the CNN-based model can take delay into account, and unlike the E-model, the resulting accuracy is much higher.Keywords: speech analysis; software defined networks; OpenFlow; artificial neural networks

sdn software defined networks epub to 21

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Abstract:The use of Software-Defined Networking (SDN) in the communications of the Industrial Internet of Things (IIoT) demands more comprehensive solutions than those developed to date. The lack of an SDN solution applicable in diverse IIoT scenarios is the problem addressed in this article. The main cause of this problem is the lack of integration of a set of aspects that should be considered in a comprehensive SDN solution. To contribute to the solution of this problem, a review of the literature is conducted in this article, identifying the main requirements for industrial networks nowadays as well as their solutions through SDN. This review indicates that aspects such as security, independence of the network technology used, and network centralized management can be tackled using SDN. All the advantages of this technology can be obtained through the implementation of the same solution, considering a set of aspects proposed by the authors for the implementation of SDNs in IIoT networks. Additionally, after analyzing the main features and advantages of several architectures proposed in the literature, an architecture with distributed network control is proposed for all SDN network scenarios in IIoT. This architecture can be adapted through the inclusion of other necessary elements in specific scenarios. The distributed network control feature is relevant here, as it prevents a single fault-point for an entire industrial network, in exchange for adding some complexity to the network. Finally, the first ideas for the selection of an SDN controller suitable for IIoT scenarios are included, as this is the core element in the proposed architecture. The initial proposal includes the identification of six controllers, which correspond to different types of control planes, and ten characteristics are defined for selecting the most suitable controller through the Analytic Hierarchy Process (AHP) method. The analysis and proposal of different fundamental aspects for the implementation of SDNs in IIoT in this article contribute to the development of a comprehensive solution that is not focused on the characteristics of a specific scenario and would, therefore, be applicable in limited situations.Keywords: industrial internet of things communications; software-defined networking; communications performance optimization; comprehensive SDN solution

Software-defined networking is an emerging field with lots of room for innovation. There have been many companies, cough BitTorrent cough, which have really managed to wrangle this topic to create great Software. Companies are often in the SDN news, take Twitch, for example, a live video gaming streaming company, requiring heavy use of strong computer networks to relay live streaming data. Developers and organizations are getting behind SDN and Node.js to build interoperability between Software applications.

Tungsten Fabric (formerly known as OpenContrail) is a secure softwaredefined networking project designed for the cloud native, multicloudenvironment. Placing it on top of any IP network allows you to have asingle portal for defining, monitoring, and analyzing your entiremulticloud network, its security, and its performance.

Radoglou-Grammatikis, Panagiotis, Rompolos, Konstantinos, Sarigiannidis, Panagiotis, Argyriou, Vasileios, Lagkas, Thomas, Sarigiannidis, Antonios, Goudos, Sotirios and Wan, Shaohua (2022) Modeling, detecting, and mitigating threats against industrial healthcare systems : a combined software defined networking and reinforcement learning approach. IEEE Transactions on Industrial Informatics, 18(3), pp. 2041-2052. ISSN (print) 1551-3203

The next generation of mobilecommunication (i.e., 5G) will bring new challengesfor the transport infrastructure, e.g. in terms offlexibility and capacity. The joint orchestration ofradio and transport resources can help to addresssome of these challenges. One example is thepossibility to reconfigure the use of the transportnetwork resources according to the spatial andtemporal variations of the wireless traffic patterns.Using the concept of dynamic resource sharing, alimited pool of transport resources can be sharedamong a large number of radio base stations (RBSs)thus reducing considerably the overall deploymentcost of the transport infrastructure.This paper proposes a provisioning strategy for acentralized radio access network (C-RAN) with anoptical transport whose wavelength resources can bedynamically shared among multiple RBSs. Theproposed strategy utilizes a hierarchical softwaredefined networking (SDN) control plane where aglobal orchestrator optimizes the usage of radio andtransport resources. The benefits of the proposedstrategy are assessed both by simulation and byexperiment via an optical data plane emulatordeveloped for this purpose. It is shown that thedynamic resource sharing can save up to 31.4% oftransport resources compared to a conventionaldimensioning approach, i.e., based onoverprovisioning of wavelength resources.

Software defined networking allows network providers to share their physical network (PN) among multiple tenants by means of network slicing, where several virtual networks (VNs) are provisioned on top of the physical one. In this scenario, PN resource utilization can be improved by introducing advanced orchestration functionalities that can intelligently assign and redistribute resources among the slices of different tenants according to the temporal variation of the VN resource requirements. This is a concept known as dynamic slicing. This paper presents a solution for the dynamic slicing problem in terms of both mixed integer linear programming formulations and heuristic algorithms. The benefits of dynamic slicing are compared against static slicing, i.e., an approach without intelligent adaptation of the amount of resources allocated to each VN. Simulation results show that dynamic slicing can reduce the VN rejection probability by more than 1 order of magnitude compared to static slicing. This can help network providers accept more VNs into their infrastructure and potentially increase their revenues. The benefits of dynamic slicing come at a cost in terms of service degradation (i.e., when not all the resources required by a VN can be provided), but the paper shows that the service degradation level introduced by the proposed solutions is very small. 2ff7e9595c