Software defined networking for telecom operators: Architecture and applications

The flexibility and programmability offered by Software Defined Networking (SDN) emerge as an approach to resolve some of the main challenges that the Internet of Things (IoT) deployments expose. In the present article, we explore the application of SDN components onto IoT deployments, together with the decoupling of typical gateway functions in two realms; the physical one and the virtual at the edge. This disengagement is a novel approach necessary since the computation resources on the physical part of the infrastructure are limited. Thus, the more resource-consuming processes are transferred to the edge of the network, in the virtual counterpart of the gateway. Additionally, within this infrastructure, different network applications can be running at the top of the SDN controller, in the control plane, for example, QoS policies application, management of network parameters, etc. Providing a holistic view of the IoT deployment. Between its advantages, this work focus on the ease of creation of an IoT infrastructure with the gateway and SDN architecture, and the behaviour of the IoT system. For validation, a small setup scenario and a use case is created where heterogeneous IoT sensors communicate with the physical part of the gateway, implemented with a Raspberry Pis and this, with its virtual part at the edge of the network, so finally sent the data to an IoT platform (FIWARE-ORION) at the core network managed by a virtual Switch (OpenVSwitch) and a RYU-based SDN controller.

Software Defined Network (SDN) Technology made tremendous changes in networking field by controlling the hardware with remote software by decoupling data plane and control plane. The focus here is on the controller which is part of control plane. A controller is a central part of SDN; it is an intermediary between Data plane and Forwarding plane. It takes prime place in SDN architecture like Operating System in Computers. SDN Controller is a software which directs the packets using flow rules and also contains statistical information of network. The controller orchestrates the networking devices such as switches and routers by channelizing the packets. SDN controller has different flavor based on the commercial requirement achieved by including new pluggable module. This paper discusses some of the SDN Controller with their feature; the controllers are POX, NOX, Floodlight, ODL, Open Contrail. This paper also compares the characteristic of centralized and distributed controller by focusing on performance, bandwidth and round-trip time of Floodlight and Open Daylight (ODL) controller. The simulation is conducted using Mininet.

The Internet has reached a breaking point, enabling ubiquitous accessibility to all digital demands. Though people have accelerated their use of overall digital demands due to the convenience of the Internet, where nearly everything is connected and accessible, it is becoming increasingly difficult to manage and modify networks. It is always a challenge to meet the emerging needs of network industry. SDN framework was proposed to overcome traditional network architecture (TNA) restrictions in order to cope with contemporary network management in terms of flexibility, agility, responding to faults, and dynamic changes. SDN assists network operators by uncoupling the control plane from its underlying ternary content-addressable memory (TCAM) hardware often called as forwarding plane by allowing the networks to programme as per the anticipated network functionality. Now, switching from traditional to software-defined networking styles, it is worth to check the robustness of the open flow (south bound interface)-based controller platform and the capability of its fault tolerance attribute to approach the successful SDN implementation. The performance of SDN open flow controllers plays a vital role in SDN implementation, which is meant for the separation between control and data plane to leverage the life of network industry. This paper comprehensively surveys the contrast between traditional network architecture (TNA) and software-defined network architecture. This research work discusses about the best feasible SDN implementation, as well as the selection of a useful controller platform for overcoming traditional network constraints and the performance assessment criteria for SDN open flow controllers. Further, the survey is continued with possible literature or work done by various researchers on performance analysis and evaluation of open flow-based SDN controllers as well as in a group of two or more controllers with respect to multifarious network condition, network criteria/parameter/metric, scaling of network load resources with most important varying network topology design. Ultimately, it strives to point ongoing research omission with respect to controller’s performance bottleneck benchmark suggesting optimized solution when it is well experimented.KeywordsTraditional network architecture (TNA)Software-defined networking (SDN)SDN open flow controllersOpen flow protocolPerformance evaluation/analysis

In recent years, SDN has evolved as a promising alternative to the traditional networking paradigm. This is because the traditional network is decentralized, making it difficult to manage and provide trouble-shooting. SDN is an approach to network management whereby the forwarding process of the data plane (DP) is disassociated from the control plane (CP). The CP is responsible for decision-making. SDN centrally control the network infrastructure and make it programmable. Communication between the two planes (data and control) is carried out by the OpenFlow (OF) protocol. The deployment of SDN was initiated by Mr. Matrin Casado and his research team in 2008. The original projects developed at Stanford University were SANE and ETHANE. Security reports and web articles suggest that SDN (Software Defined Network) has the caliber to defend DDoS attacks. The year 2017 has been declared as the year of SDN adaption and DDoS mitigation. The initial effort in the direction of compiling the detection and mitigation approaches has been done in (Dayal et al., 2016). Recently a similar effort has been done in (Rochak et al., 2019). The CP of SDN has a complete visibility of the network which makes traffic monitoring feasible. Moreover, SDN open flow switches contain forwarding logic only; decision-making capability is softwarized at 58controller. This helps the controller to instruct and configure the switches with new flow rules which the switches follow. This facilitates the network admins to detect and mitigate these attacks easily. SDN have gained momentum among the possible solutions of DDoS attacks due to its decentralized nature of network management, but solutions come with its repercussions. The centralization and open architecture framework makes SDN difficult for widespread adaptation. The chapter begins with an introduction to DDoS attacks and its classification such as volumetric attack, protocol exploitation attack and application attack. In the next section, we discuss the possible DDoS attacks over SDN framework. We have categorized the vulnerability into three classes: DDoS attack on data, control, and application planes (APs). The DDoS attack on DP can further be classified into TCAM exhaustion, switch DDoS, ICMP flood, TCP flood, TCP_SYN flood, etc. We would discuss Resource depletion, OF bandwidth exhaustion, amplification attacks in DDoS attack over CP. HTTP flooding and Slowloris attack has been discussed in DDoS attack over AP. The next section highlights the working of attack tools used in performing DDoS attacks. Various DDoS tools have been widely exploited to render DDoS attacks and facilitate attackers to carry out dangerous attack on target; here target may be network resources, server resources, or applications. The fourth section focuses on the state-of-the-art for detection and mitigation approaches of DDoS attacks. Detection approaches are classified into statistical, policy, and machine learning-based approaches. DDoS mitigation OF switches, including Alcatel Lucent’s OmniSwitch, Brocade’s MLXe series routers, and applications such as DefensePro by Radware that are available in market; also depend on the statistical threshold to detect the DDoS attack. In addition, this section also incorporates a brief analysis of the previous defense mechanism of the last 10 years and analyzes and compares the products of different security vendors like Cloudflare, Radware, Akamai, Arbor, Nexusguard, etc.

Software Defined Networking (SDN) is a dynamic architecture that employs a variety of applications for making networks more adaptable and centrally controlled. It is easy to attack the entire network in SDN because the control plane and data plane are separated. DDoS attack is major danger to SDN service providers because it can shut down the entire network and stop services to all customers at any time. One of the key flaws of most SDN architectures is lack of susceptibility to DDoS attacks with its types like TCP flooding, UDP flooding, SYN flooding, ICMP flooding and DHCP flooding for detecting those kinds of attacks. The machine learning algorithms are widely used in recent years to identify DDoS attacks. This research utilizes Decision Tree Classifier for detection and classification of DDoS attacks on SDN. The Forward Feature Selection technique is also used in the research to select the best features from the dataset and from that dataset the data are employed to train and test the model by Decision Tree Classifier Algorithm. The decision Tree Classifier technique is a supervised method used to forecast desired values of observations using rudimentary machine learning decision rules derived from training data. Based on the accuracy of decision tree techniques, in future, a hybrid learning model will be designed for detecting the Distributed Denial of Services in an SDN environment with high accuracy and a low false negative rate.

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.

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.

Security elements of data communication networks such as packet filters, firewalls, intrusion detection and prevention systems are in first line of defense against cyber security threats and various attacks such as DDoS and many others. With constantly increasing demand of compute power and grow of various cloud services, complexity of traditional networks makes it more difficult to maintain security policies in fast and safe manner. To simplify management and overcome limitations of traditional networks concept of software-defined networking (SDN) was proposed. The main idea of SDN is separation of control and data planes of devices. Combined control plane into network controller brings intelligence into network and open wider possibilities for automation. Separated data plane makes it possible for devices to run in headless mode during critical situations when control plane is not functioning. SDN also simplifies managing of security policies, it helps to ensure intime deploying and minimize possibility of mistake. However, SDN architecture has some places which can be used as main point of cyber-attack. Purpose of this paper is to describe security benefits and drawbacks of software-defined network.

Artificial intelligence based load balancing in SDN: A comprehensive survey

Through network programmability, we may simplify network management and bring innovation, cloud computing introduced some of its network concepts. One of the most prominent cloud models for minimizing maintenance obligations and simplifying network infrastructure administration is the SDN (Software Defined Network) architecture. SDN stands out because it provides separation of the control plane and programmability for developing network applications. As a result, SDN is expected to enable more efficient configuration, higher performance, and increased flexibility to support new network architectures. This article is aimed to demonstrates the importance of the SDN and the major role it plays in the organization and how SDNs can be profitable to many organizations that remain in the archaic or a traditional cloud environment and how SDN can restructure the cloud architecture with more security enhancement and also to investigate SDN related issues and challenges to provide insight into the obstacles that this revolutionary network paradigm will face in the future, from both a protocol and architecture standpoint. In this study, systematic literature was conducted and descriptive was used to analyze data. When it comes to SDN, the following challenges and issues stand out: All of these phrases are used to characterize the properties of a system: scalability, high availability, reliability, elasticity, security, performance, resilience, and dependability.

Software-defined networking (SDN) attracts the attention of the research community in recent years, as evidenced by a large number of survey and review papers. The architecture of SDN clearly recognizes three planes: application, control, and data plane. The application plane executes network applications; control plane regulates the rules for the entire network based on the requests generated by network applications; and based on the set rules, the controller configures the switches in the data plane. The role of the switch in the data plane is to simply forward packets based on the instructions given by the controller. By analyzing SDN-related research papers, it is observed that research, from the very beginning, is insufficiently focused on the data plane. Therefore, this paper gives a comprehensive overview of the data plane survey with particular emphasis on the problem of programmability and flexibility. The first part of the survey is dedicated to the evaluation of actual data plane architectures through several definitions and aspects of data plane flexibility and programmability. Then, an overview of SDN-related research was presented with the aim of identifying key factors influencing the gradual deviation from the original data plane architectures given with ForCES and OpenFlow specifications, which we called the data plane evolution. By establishing a correlation between the treated problem and the problem-solving approaches, the limitations of ForCES and OpenFlow data plane architectures were identified. Based on identified limitations, a generalization of approaches to addressing the problem of data plane flexibility and programmability is made. By examining generalized approaches, open issues have been identified, establishing the grounds for future research directions proposal.

The software-defined network (SDNs) architecture can easily be attacked by a malicious user in order to prevent an acceptable level of service. Therefore, SDN security is a hot research topic to improve the SDN architecture and to protect the service level of the SDN components. In this letter, the INSPECTOR is a hardware device added to the SDN architecture to protect a compromised controller from a packet injection attack by verifying the authentication of Packet-In Messages accessing network resources. With simulations, we show that the INSPECTOR device efficiently stops the attack and enhances the controller performance under malicious attack.

Driven by the requirement of increasing performance and flexibility, networks are being softwarized by paradigms such as software-defined networking (SDN) and network function virtualization (NFV). These solutions reduce the complexity and the specialization of hardware devices, by extracting the inherently distributed control plane of forwarding network elements such as switches and routers, to a logically centralized control plane (referred as controller in SDN). The control plane acts as a broker between the network applications (e.g. monitoring, traffic engineering) and the data plane (i.e. physical network infrastructure). For scalability and robustness, the logically centralized control plane is implemented by physically distributing different controllers throughout the network. This chapter presents different solutions to increase the reliability of both planes: data and control planes. The reliability of the data plane can be increased by considering survivable virtual network embedding solutions. This chapter proposes a survivable embedding against single and double failures at either links or nodes. Furthermore, in order to provide a programmable and resilient data plane, BPFabric has been proposed for SDN which supports high performance functions suitable for detecting attacks. On the other hand, the reliability of the control plane applied to SDN can be addressed by considering enhanced controller placement solutions providing redundancy against uncorrelated as well as targeted failures while coping with latency and capacity requirements. Furthermore, a solution to increase the security and robustness of the control channel is also addressed in this chapter.

Distributed Denial of Service (DDoS) attacks represent a major concern in modern Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in the whole SDN architecture. Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (DAD), specifically focusing on Transmission Control Protocol SYN flood attacks. We compare two different DAD architectures, called Standalone and Correlated DAD, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time DAD. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of upmu text {s} in the worst case. Considering real-time DAD implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language.Graphic

The nature of computer network flash crowd traffic, which is generated by legitimate users accessing servers or other network resources are similar to the traffic generated by Distributed Denial of Service (DDoS) like attacks. With advancement in spoof packet generation tools, attacker may generate Multi-source Multi-destination Multi-protocol (MMM) traffic; characteristics of such traffic are very similar to on-going genuine/ flash crowd traffic in the network. In the case of Software Defined Network (SDN), attacker’s target is controller plane. Controller plane in SDN is a centralized processing unit of the underlying network, which manages several data planes. Controller plane frames the policies and pushes forwarding rules to the data planes. Data planes just maintain the forwarding rules. Thus by overloading the SDN controller, functionality of complete computer network will be hampered. In this paper, we have proposed Media Access Control (MAC) address based Model to Differentiate Flash crowd and Malicious traffic in SDN (MDFMS). Novelty of the proposed model is to detect, locate and mitigate the source of Traditional DDoS (T-DDoS) and MMMDDoS traffic. MDFSM has been implemented on separate machine to avoid any additional computing load on SDN controller. It also preserves the original design of the SDN architecture. Proposed model has been evaluated under various scenarios and encouraging results have been obtained to differentiate T-DDoS and MMM-DDoS from benign flash crowd traffic.