Software-Defined Networking: Concepts and Applications

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.

In traditional networking devices, there is a tight coupling of data plane and control plane. It handles a huge volume of traffic on the Internet which is not possible in traditional networking devices. The strategy emerged of disassociating the control plane from the data plane is defined as software-defined networking (SDN). The rapid increase in the number of Internet users is becoming a difficult task for conventional load balancers to handle network traffic. SDN provides power over the network from the centralized controller to distinguish between the control plane and the data plane. SDN is a prominent technology in today’s Internet. Nowadays, our networks handle the extensive amount of traffic that serves the hundreds and thousands of clients where it is very difficult for the single server to handle such a huge load. To overcome this problem, the load balancing technique came into existence. The main purpose of this work is to show how SDN applications can be created without using any controller and analyze the efficiency/effectiveness of load balancing algorithms based on SDN.

A major recent development in computer networking is the emergence of Software Defi ned Networking (SDN), whose goal is to provide a centralized, programmable control plane that is decoupled from the distributed data planes on individual network devices. In particular, the development of OpenFlow has demonstrated many potential benefits of SDN, and multiple vendors have started to offer commercial switches supporting the OpenFlow standard. Researchers have also made progress on SDN components including SDN controllers, switches, programming interfaces, verification and debugging tools, and SDN applications in data center networks and campus networks. Despite the progress, many important issues regarding SDN remain. The goal of this Special Topic Issue is to explore research and developments related to SDN to further its state of art.

Software Defined Networking (SDN) is widely used in many practical contexts and provides a method for managing networks by separating the data plane from the control plane. However, because of its centralized control logic, SDN is particularly susceptible to Distributed Denial of Service (DDoS) attacks. In numerous studies, methods based on Machine Learning (ML) have been proposed to identify DDoS attacks in the context of SDN. However, the proposed methods are resource intensive and unreliable in a large-scale SDN where a huge amount of traffic data is produced from control and data planes. This may exhaust computational resources, deteriorate the performance of the network, or even result in the shutdown of network systems as a result of resource exhaustion. To deal with the above issues, this paper presents a real-time DDoS detection and mitigation method in a multi-controller SDN environment using cloud computing. We propose to run the Machine Learning development cycle on a secured server in a virtual machine. Then we deploy back the trained model into the SDN controllers. All the exchanges between the virtual machine and the controllers are secured. In this way, we configure these controllers to detect and mitigate DDoS in SDN in real-time. Through useful experiments, we demonstrate that our method is secure, reliable, and efficient.

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.

Software Defined Networking (SDN) is one of the key enablers for evolving mobile network architecture towards 5G. SDN involves the separation of control and data plane functions, which leads in the context of 5G, to consider the separation of the control and data plane functions of the different gateways of the Evolved Packet Core (EPC), namely Serving and Packet data Gateways (S and P-GW). Indeed, the envisioned solutions propose to separate the S/P-GW into two entities: the S/P-GW-C, which integrates the control plane functions; and the S/P-GW-U that handles the User Equipment (UE) data plane traffic. There are two major approaches to create and update user plane forwarding rules for such a partition: (i) considering an SDN controller for the S/P-GW-C (SDNEPC) or (ii) using a direct specific interface to control the S/P-GW-U (enhancedEPC). In this paper, we evaluate, using a testbed, those two visions against the classical virtual EPC (vEPC), where all the elements of the EPC are virtualized. Besides evaluating the capacity of the vEPC to manage and scale to UE requests, we compare the performances of the solutions in terms of the time needed to create the user data plane. The obtained results allow drawing very interesting remarks, which may help to dimension the vEPC's components as well as to improve the S/P-GW-U management procedure.

The expected higher complexity of upcoming 5G wireless backhauls suggests the need to evolve towards a softwaredefined networking (SDN) paradigm to increase the degree of programmability of these networks. Within the context of softwaredefined wireless backhaul, one important issue is the provision of an even resource consumption of both network and IT resources. Backpressure policies have shown their ability to balance resource consumption in traditional (non-SDN) wireless backhauls. This paper analyzes two use cases in which backpressure policies can be integrated in software-defined wireless backhaul to manage network and IT resources. The first use case proposes an SDN application based on a centralized backpressure policy to balance network resources in the wireless backhaul. Simulation results reveal how the granularity of routing decisions in the SDN application significantly affects the data plane performance, suggesting a trade-off between data plane performance and overload in the control plane. The second use case proposes a distributed backpressure policy to deal with the management of computing resources by balancing the processing load caused by OpenFlow (OF) switch requests among the available distributed SDN controllers. Simulation results demonstrate how a dynamic and distributed backpressure policy can balance the processing load amongst different SDN controllers, hence, leading to significant improvements with respect to static mapping policies.

The evolution and advancements in Information and Communication Technologies (ICT) have enabled large scale distributed computing with a huge chunk of applications for massive number of users. This has obviously generated large volumes of data, thus severely burdening the processing capacity of computers as well as the inflexible traditional networks. State-of-the-art methods for addressing datacenter level performance fixes are yet found wanting for sufficiently addressing this huge processing, storage, and network movement with proprietary protocols for this voluminous data. In this chapter the works have focused on addressing the backend server performance through effective reducer placement, intelligent compression policy, handling slower tasks and in-network performance boosting techniques through effective traffic engineering, traffic classification, topology discovery, energy minimization and load balancing in datacenter-oriented applications. Hadoop, the defacto standard in distributed big data storage and processing, has been designed to store data with its Hadoop Distributed File System (HDFS) and processing engine MapReduce large datasets reliably. However, the processing performance of Hadoop is critically dependent on the time taken to transfer data during the shuffle generated during MapReduce. Also, during concurrent execution of tasks, slower tasks need to be properly identified and efficiently handled to improve the completion time of jobs. To overcome these limitations, three contributions have been made; (i) Compression of generated map outputs at a suitable time when all the map tasks are yet to be completed to shift the load of network onto the CPU; (ii) Placing the reducer onto the nodes where the computation done is highest based on a couple of counters, one maintained at the rack level and another at node level, to minimize the run-time data copying; and (iii) Placing the slower map tasks onto the nodes where the computation done is highest and network is handled by prioritizing. Software defined networking (SDN) has been a boon for next generation networking owing to the separation of control plane from the data plane. It has the capability to address the network requirements in a timely manner by setting flows for every to and fro data movement and gathering large network statistics at the controller to make informed decisions about the network. A core issue in the network for the controller is traffic classification, which can substantially assist SDN controllers towards efficient routing and traffic engineering decisions. This chapter presents a traffic classification scheme utilizing three classifiers namely Feed-forward Neural Network (FFNN), Logistic Regression (LR), Naïve Bayes and employing Particle Swarm Optimization (PSO) for improved traffic classification with less overhead and without overlooking the key Quality of Service (QoS) criterion. Also lowering energy minimization and link utilization has been an important criterion for lowering the operating cost of the network and effectively utilizing the network. This issue has been addressed in the chapter by formulating a multi-objective problem while simultaneously addressing the QoS constraints by proposing a metaheuristic, since no polynomial solution exists and hence an evolutionary based metaheuristic (Clonal Selection) based energy optimization scheme, namely, Clonal Selection Based Energy Minimization (CSEM) has been devised. The obtained results show the efficacy of the proposed traffic classification scheme and CSEM based solution as compared with the state-of-the-art techniques. SDN has been a promising newer network paradigm but security issues and expensive capital procurement of SDN limit its full deployment hence moving to a hybrid SDN (h-SDN) deployment is only logical moving forward. The usage of both centralized and decentralized paradigms in h-SDN with intrinsic issues of interoperability poses challenges to key issues of topology gathering by the controller for proper allocation of network resources and traffic engineering for optimum network performance. State-of-the-art protocols for topology gathering, such as Link Layer Discovery Protocol (LLDP) and Broadcast Domain Discovery Protocol (BDDP) require a huge number of messages and such schemes only gather link information of SDN devices leaving out legacy switches’ (LS) links which results in sub-optimal performance. This chapter provides novel schemes which unearth topology discovery by requiring fewer messages and gathering link information of all the devices in both single and multi-controller environments (might be used when scalability issue is prevalent in h-SDN). Traffic engineering problems in h-SDN are addressed by proper placement of SDN nodes in h-SDN by utilizing the analyzing key criterion of traffic details and the degree of a node while lowering the link utilization in real-time topologies. The results of the proposed schemes for topology discovery and SDN node placement demonstrate the merits as compared with the state-of-the-art protocols.

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 revolutionizing network architecture by offering incredible scalability, flexibility, and programmability. SDN's applications have expanded into various domains, including cloud computing, edge computing, enterprise networks, data centers, and the Internet of Things. One of its standout features is its ability to enhance traffic engineering. Traffic engineering involves optimizing and dynamically controlling traffic flows to reduce congestion and minimize the maximum link utilization, all while managing network traffic from a centralized point. SDN boosts overall network performance, improves user experience, and ensures that resources are used effectively. Minimizing maximum link utilization means distributing network traffic across different links or paths to make use of the most available resources and avoid congestion. This process is often referred to as load balancing or congestion minimization within the network. The primary aim is to prevent any single link from becoming overloaded. Over the years, various algorithms have been proposed to tackle this issue, but still such algorithms struggle with effectively minimizing the maximum link utilization. The proposed work achieves approximately 24% and 16% improvement in load balancing; it reduces congestion by 94% and 60% when compared to tabu search and enhanced tabu search.

Software-defined network (SDN) radically changes the network architecture by decoupling the network logic from the underlying forwarding devices. This architectural change rejuvenates the network-layer granting centralized management and reprogrammability of the networks. From a security perspective, SDN separates security concerns into control and data plane, and this architectural recomposition brings up exciting opportunities and challenges. The overall perception is that SDN capabilities will ultimately result in improved security. However, in its raw form, SDN could potentially make networks more vulnerable to attacks and harder to protect. In this paper, we provide a comprehensive review of SDN security domain while focusing on its data plane, which is one of the least explored but most critical aspects in securing this technology. We review the most recent enhancements in SDNs, identify the main vulnerabilities of SDNs, and provide a novel attack taxonomy for SDNs. Thereafter, we provide a comprehensive analysis of challenges involved in protecting SDN data plane and control plane and provide an in-depth look into available solutions with respect to the identified threats and identify their limitations. To highlight the importance of securing the SDN platform, we also review the numerous security services built on top of this technology. We conclude the paper by offering future research directions.

Cloud and parallel computing are fundamental components in the processing of large data sets. Deployments of distributed computers require network infrastructure that is fast, efficient, and secure. Software Defined Networking (SDN) separates the forwarding of network data by switches (data plane) from the setting and managing of network policies (control plane). While this separation provides flexibility for setting network policies affecting the establishment of network flows in the data plane, it provides little to no fault tolerance for failures, either benign or caused by corrupted/malicious applications. Such failures can cause network flows to be incorrectly routed through the network or stop such flows altogether. Without protection against faults, cloud network providers using SDN run the risk of inefficient allocation of network resources or even data loss. Furthermore, the asynchronous nature existing protocols for SDN does not provide a mechanism for consistency in network policy updates across multiple switches.In addition, cloud and parallel applications require an efficient means for accessing local system data (input data sets, temporary storage locations, etc.). While in many cases it may be possible for a process to access this data by making calls directly to a file system (FS) kernel driver, this is not always possible (e.g. when using experimental distributed FSs where the needed libraries for accessing the FS only exist in user space).This dissertation provides a design for fault tolerance of SDN and infrastructure for advancing the performance of user space FSs. It is divided into three main parts. The first part describes a fault tolerant, distributed SDN control plane framework. The second part expands upon the fault tolerant approach to SDN control plane by providing a practical means for dynamic control plane membership as well as providing a simple mechanism for controller authentication through threshold signatures. The third part describes an efficient framework for user space FS access.This research makes three contributions. First, the design, specification, implementation, and evaluation of a method for fault tolerant SDN control plane that is inter-operable with existing control plane applications involving minimal instrumentation of the data plane runtime. Second, the design, specification, implementation and evaluation of a mechanism for dynamic SDN control plane membership that all ensure consistency of network policy updates and minimizes switch overhead through the use of distributed key generation and threshold signatures. Third, the design, specification, implementation, and evaluation of a user space FS access framework that is correct to the Portable Operating System Interface (POSIX) specification with significantly better performance over existing user space access methods, while requiring no implementation changes for application programmers.

Network is dynamic and requires update in the operation. However, many confusions and problems can be caused by careless schedule in the update process. Although the problem has been investigated for many years in traditional networks where the control plane is distributed, software defined networking (SDN) brings new opportunities and solutions to this problem by the separation of control and data plane, as well as the centralized control. This paper makes a survey on the problems caused by network update, including forwarding loop, forwarding black hole, link congestion, network policy violation, etc., as well as the state-of-the-art SDN solutions to these problems. Furthermore, we summarize the network configuration strength and discuss the open issues of network update in the SDN paradigm.

Software-Defined networking (SDN) is a networking paradigm to enable dynamic, flexible, and programmatically efficient configuration of networks to revolutionize network control and management via separation of the control plane and data plane. The SDN technology has evolved in response to the demands from large data centers toward all types of networks, from IoT, enterprise, to ISP networks. On the one hand, SDN has provided solutions for high-demand resources, managing unpredictable data traffic patterns, and rapid network reconfiguration. It is further used to enhance network virtualization and security. On the other hand, SDN is still subject to many traditional network security threats. It also introduces new security vulnerabilities, primarily due to its logically centralized control plane infrastructure and functions. In this paper, we conduct a comprehensive survey on the core functionality of SDN from the perspective of secure communication infrastructure at different scales. A specific focus is put forward to address the challenges in securing SDN-based communications, with efforts taken up to address them. We further categorize the appropriate solutions for specific threats at each layer of SDN infrastructures. Lastly, security implications and future research trends are highlighted to provide insights for future research.

Operational, organizational and business challenges for network operators in the context of SDN and NFV