Realizing video streaming multicast over SDN networks

Several algorithms have been proposed to provide better Quality of Service (QoS) in networks. Software Defined Networks (SDN) is a new approach in networking that is revolutionizing the traditional networks. Mechanisms such as differentiated services, admission control and traffic engineering methods are used to provide QoS in SDN networks the same as the traditional networks. In addition, because of the programmability of the SDN networks and centralized control on network switches, the ability to implement many of these algorithms is provided. In this paper a new method is presented to provide better Quality of Service in SDN networks using differentiated services. In this method flows entering the network are dynamically assigned to DiffServ Queues at the output port. If needed, flows are moved between the queues so that while preventing increasing delays of existing flows new flows can be accepted by using available capacities of other queues. Simulation results show that in this way, the network utilization is increased.

The Internet has caused the advent of a digital society; wherein almost everything is connected and available from any place. Thus, regardless of their extensive adoption, traditional IP networks are yet complicated and arduous to operate. Therefore, there is difficulty in configuring the network in line with the predefined procedures and responding to the load modifications and faults through network reconfiguring. The current networks are likewise vertically incorporated to make matters far more complicated: the control and data planes are bundled collectively. Software-Defined Networking (SDN) is an emerging concept which aims to change this situation by breaking vertical incorporation, promoting the logical centralization of the network control, separating the network control logic from the basic switches and routers, and enabling the network programming. The segregation of concerns identified between the policies concept of network, their implementation in hardware switching and data forwarding is essential to the flexibility required: SDN makes it less complicated and facilitates to make and introduce new concepts in networking through breaking the issue of the network control into tractable parts, simplifies the network management and facilitate the development of the network. In this paper, the SDN is reviewed; it introduces SDN, explaining its core concepts, how it varies from traditional networking, and its architecture principles. Furthermore, we presented the crucial advantages and challenges of SDN, focusing on scalability, security, flexibility, and performance. Finally, a brief conclusion of SDN is revised.

The DFG project “SDN-enabled Application-aware Network Control Architectures and their Performance Assessment” (DFG SDN-App) focused in phase 1 (Jan 2017 – Dec 2019) on software defined networking (SDN). Being a fundamental paradigm shift, SDN enables a remote control of networking devices made by different vendors from a logically centralized controller. In principle, this enables a more dynamic and flexible management of network resources compared to the traditional legacy networks. Phase 1 focused on multimedia applications and their users’ Quality of Experience (QoE). This documents reports the achievements of the first phase (Jan 2017 – Dec 2019), which is jointly carried out by the Technical University of Munich, Technical University of Berlin, and University of Wurzburg. The project started at the institutions in Munich and Wurzburg in January 2017 and lasted until December 2019. In Phase 1, the project targeted the development of fundamental control mechanisms for network-aware application control and application-aware network control in Software Defined Networks (SDN) so to enhance the user perceived quality (QoE). The idea is to leverage the QoE from multiple applications as control input parameter for application-and network control mechanisms. These mechanisms are implemented by an Application Control Plane (ACP) and a Network Control Plane (NCP). In order to obtain a global view of the current system state, applications and network parameters are monitored and communicated to the respective control plane interface. Network and application information and their demands are exchanged between the control planes so to derive appropriate control actions. To this end, a methodology is developed to assess the application performance and in particular the QoE. This requires an appropriate QoE modeling of the applications considered in the project as well as metrics like QoE fairness to be utilized within QoE management. In summary, the application-network interaction can improve the QoE for multi-application scenarios. This is ensured by utilizing information from the application layer, which are mapped by appropriate QoS-QoE models to QoE within a network control plane. On the other hand, network information is monitored and communicated to the application control plane. Network and application information and their demands are exchanged between the control planes so to derive appropriate control actions.

The Software Defined Networking (SDN) is an innovative network architecture that offers flexible and programable networks through a centralized controller. However, If the controller fails the whole system becomes paralyzed. The Distributed Denial of Service (DDoS) attack is one of the main threats to the SDN controller, as it exhausts the resources of the SDN controller which disturbs the whole network and affects the performance of the network. In this regard, we propose a Support Vector Machine (SVM) based machine learning classifier to detect the DDoS attacks on SDN controller. Once the attack is detected by the classifier, mitigation module is invoked to block the attack flows. The objective is to detect DDoS attacks with high accuracy and mitigate them in a short period of time. We evaluate our proposed solutions on two different publicly available datasets. i.e., KDD and KDD’99

Software defined networking (SDN) decouples the control plane from the data plane of forwarding devices. This separation provides several benefits, including the simplification of network management and control. However, due to a variety of reasons, such as budget constraints and fear of downtime, many organizations are reluctant to fully deploy SDN. Partially deploying SDN through the placement of a limited number of SDN devices among legacy (traditional) network devices, forms a so called hybrid SDN network. While hybrid SDN networks provide many of the benefits of SDN and have a wide range of applications, they also pose several challenges. These challenges have recently been addressed in a growing body of literature on hybrid SDN network structures and protocols. This paper presents a comprehensive up-to-date survey of the research and development in the field of hybrid SDN networks. We have organized the survey into five main categories, namely hybrid SDN network deployment strategies, controllers for hybrid SDN networks, protocols for hybrid SDN network management, traffic engineering mechanisms for hybrid SDN networks, as well as testing, verification, and security mechanisms for hybrid SDN networks. We thoroughly survey the existing hybrid SDN network studies according to this taxonomy and identify gaps and limitations in the existing body of research. Based on the outcomes of the existing research studies as well as the identified gaps and limitations, we derive guidelines for future research on hybrid SDN networks.

Software Defined Networking (SDN) has come up as a promising technology for making the communication networks more flexible and dynamically (re)configurable. With the advent of fifth generation (5G) of mobile networks, the aforementioned flexibility became more urgent and critical than ever. SDN offers the decoupling of the network's control plane from the data plane. As a result, the infrastructure layer can be abstracted from the application and network services and therefore the network control can be done completely through a centralized component/orchestrator. In this work, a detailed practical deployment of an SDN controller for a converged FiberWireless (Fi-Wi) 5G network is presented, followed by the SDN architecture and functional components. Lastly, an experimental trial using an SDN controller directly connected to Fi-Wi lab equipment is described, in which the physical layer parameters are managed and monitored successfully, proving the applicability of SDN in a Fi-Wi network.

Background. The implementation of the new modern services on existing networks requires replacement or modernization of the old equipment. This negatively affects the efficiency of providing users with new types of services and their cost. Therefore, the issues/tasks of implementing SDN technology in the construction of networks are in the center of attention of representatives of science-research organizations and telecom operators. SDN networks have the advantages of flexible scaling of the system without replacing existing server and network equipment, eliminating operator dependence on specific vendor solutions, and rapid implementation of the new network technologies and services. To determine the numerical values of indicators of the functioning of SDN networks and check their compliance with the requirements, it is proposed to use in working process simulation modelling. For these goals, the Open Networking Foundation consortium developed the Mininet network emulator, the elements of that are published publicly. The formation of the simulation model and the procedure for determining the performance indicators of the SDN network that uses Mininet has a number of features that are considered in the work. Objective. The purpose of the article is to build a simulation model based on the Mininet network emulator and to determine the performance indicators of SDN networks of various structures/topologies. Methods. Creation of a SDN network segment for testing process of its functioning in the overload mode of various communication routes. Results. The Mininet network emulator allows simulating SDN networks of a rather complex structure, change the performance of network branches and the amount of load in communication routes, to create a network-wide overload mode. During the simulation, it is possible to determine a number of indicators of network performance, such as the RTT (Round Trip Time) parameter for each route of communication, the bandwidth of branches and routes of communication, the amount of delay on network elements, loading of OpenFlow Switch ports/interfaces, network elements with the highest delay, the number of served and lost packets. Conclusions. Use of the Mininet network emulator is a fairly convenient tool for determining the performance indicators of SDN networks. However, there are also some problems. First of all, setting up the system's operability requires writing programs to ensure the interaction among standard elements from the Mininet library. Secondone, is when congestion occurs in the communication routes, the Floodlight Controller does not automatically balance traffic along the work-around route. This task has to be solved manually by making changes to the OpenFlow Switch routing tables. Keywords: Software Defined Networking; SDN; Mininet; OpenFlow; Floodlight Controller; OpenFlowSwitch; NFV; virtualization.

This article addresses the joint switch and controller placement problem in a hybrid software-defined network (SDN), which is an intermediate step of transforming a traditional backbone network into a pure SDN. For a hybrid SDN, the quality of service (QoS) is a primary concern for ensuring service guarantee of a traditional network, while providing additional benefits of softwarization. In the SDN, the positions of SDN controllers determine the QoS parameters, such as network throughput and flow processing delays. Many existing solutions determine the controller locations to optimize the latency of network links and control plane load. However, there is a need to address the problem considering additional aspects of the hybrid SDN, such as SDN switch placement and incremental upgrades. In this article, we propose a priority-based SDN switch placement approach and an incremental controller placement approach for the hybrid SDN. Simulation results depict that the proposed scheme increases programmable traffic by 9.90% compared to the existing MapFirst scheme when the switch upgrade budget is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> 400 K. The proposed scheme decreases QoS violation by 20.89% compared to the Greedy approach with 0.3 million flows.

It is Software defined networking (SDN) is a new network approach which allows separation of the forwarding plane from the control plane in network architecture, that aids in the efficient control of data flow and gives network administrators a software- based method for managing and configuring the networks. In contrast to SDN, which provides a centralized controller, traditional network topologies require the individual configuration of each network device. Due to a centralized control functionality, SDN is increasingly used by businesses, telecom service providers, cloud providers, and many other users, as it encourages flexibility and scalability in the network while also assisting in streamlining operations. There is significant demand of SDN for effective and adaptable networks. SDN adoption has also been rising in the campuses and corporate sectors. This paper provides a concise overview of the Software Defined Networks (SDN) topic, which is essential for comprehending the fundamental principles of SDN in order to conduct research and implement SDN in many sectors. The initial focus is on the discussion of traditional networks, including their limitations, as well as the many issues encountered by industries. The topic of Software Defined Networks is explored, encompassing their necessity, historical background, architectural structure, interfaces, and models. Next, the many applications of Software-Defined Networking (SDN) are examined. Additionally, the various SDN case studies, vendors, setup issues with SDN, and current research fields were elucidated. The discussion focuses on the role of Software-Defined Networking (SDN) in Artificial Intelligence (AI), specifically addressing the issues associated with implementing SDN in AI.

The new ecosystems of ICTs are changing, dynamic and diverse, so that traditional networks must evolve towards a new centralized, scalable and resilient management that is beyond the boundaries of the data center, reaching the wide area network service providers. However, this transition entails the deployment of new scenarios in which traditional technology and software-defined networks (SDNs) converge, giving rise to various hybrid scenarios. In this paper new cloud ecosystems and Internet of Things (IoT) are implemented in a real environment, applying the concept of living laboratory, which allows to evaluate the hyperconvergence of SDN-WAN and hybrid cloud as network services.

Organizations face a challenge of accurately analyzing network data and providing automated action based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and improve the performance of the network services, but organizations use different network management tools to understand and visualize the network traffic with limited abilities to dynamically optimize the network. This research focuses on the development of an intelligent system that leverages big data telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trend-based networking decisions. The results include a graphical user interface (GUI) done via a web application for effortless management of all subsystems, and the system and application developed in this research demonstrate the true potential for a scalable system capable of effectively benchmarking the network to set the expected behavior for comparison and trend analysis. Moreover, this research provides a proof of concept of how trend analysis results are actioned in both a traditional network and a software-defined network (SDN) to achieve dynamic, automated load balancing.

Advantages of Software Defined Networks (SDN) technology, which decouples control and transmission planesof computer networks, provides the infrastructure that can help to improve the system performance of video streaming applications, one of today's popular Internet applications. The SDN controller may assist the clients by using the network statistics while determining the routes of the appropriate streaming paths in the physical network. In this study, a layered video streaming architecture is proposed, in which SDN controller recommends a server, video codec, and the optimal number of video layers to clients, with taking into account the server load and network status. Performance of the proposed system is given comparatively and it has been shown that the system parameters considered can help to improve the video streaming system performance.

In last few years there has been significant growth in the area of wireless communication. Quality of Service (QoS) has become an important consideration for supporting variety of applications that utilize the network resources. These applications include voice over IP, multimedia services, like, video streaming, video conferencing etc. IEEE 802.16/WiMAX is a new network which is designed with quality of service in mind. This paper focuses on analysis of quality of service as implemented by the WiMAX networks. First, it presents the details of the quality of service architecture in WiMAX network. In the analysis, a WiMAX module developed based on popular network simulator ns-2, is used. Various real life scenarios like voice call, video streaming are setup in the simulation environment. Parameters that indicate quality of service, such as, throughput, packet loss, average jitter and average delay, are analyzed for different types of service flows as defined in WiMAX. Results indicate that better quality of service is achieved by using service flows designed for specific applications.

In this work, a traditional network or a hardware defined network is compared to a software defined network given a specific backbone topology. Authors choose REUNA, the advanced network for Chile, a dynamic backbone topology which has evolved respect to backbone routers and bandwidth. GNS3 is used to emulate REUNA backbone topology as a traditional physical environment, on the other side Mininet is used to emulate SDN environment, both under IPv6 protocols. Fully connectivity and then streaming were tested detailing as parameters CPU, RAM, and BW usage. Results shows capabilities and limitations for GNS3 and Mininet when comparing both implementation environments, but also pros and cons for SDN and traditional networks.

The burgeoning demands of the Internet of Things (IoT) applications such as video/audio streaming in surveillance, disaster recovery, multimedia, and healthcare paves the need to provide better Quality of Service (QoS) delivery. The growth in the amount of data generated by multimedia applications increases congestion in the network. Software-Defined Networking (SDN) is an emerging approach that centrally controls the network and solves the congestion problem in the network. SDN has many advantages that help in network management, traffic shaping, and routing in the IoT network to manage the data generated from a diverse range of applications. In this paper, we propose a congestion technique using Hierarchical Token Bucket (HTB) to manage the bandwidth in the Software-Defined IoT (SDIoT) network. Further, we propose a routing scheme to compute optimal routing shortest paths using Dijkstra’s algorithm by selecting the min-cost path based on the priorities of traffic flows. The results illustrate that the proposed approach achieves a reduction in end-to-end delay by 38%, 44% and higher average throughput by 29%, 43% in comparison with the benchmark schemes - SDN with HTB and the Delay Minimization method, respectively.