Overview of SDN-Based Traffic Engineering

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.

Network Virtualization (NV) and Software Defined Networking (SDN) bring new business opportunities for network providers allowing them to offer the network as a service over their network infrastructure. One challenge for network providers is to provision virtual SDN services for multiple tenants while satisfying QoS and traffic engineering (TE) objectives of the higher priced SDN services, allocating the network resources cost effectively and managing the virtual services that span over customer premises, wide area and cloud domains. In this paper, we describe an algorithmic framework that includes QoS management and traffic-engineering methods for managing virtual services over large SDN networks. The algorithmic framework is part of an intelligent service management architecture which employs optimization algorithms, machine learning and artificial intelligence techniques into the management of virtual services over SDN networks.

Research Subject - integration of software-defined networks (SDN) and network functions virtualization (NFV) technologies in the unmanned aerial vehicle networks. Method – analysis of standards and recommendations in the field of SDN, NFV technologies and construction of UAV networks. Core results – presents the main advantages of introducing SDN and NFV technologies into the UAV networks, two ways of organizing the UAV network architecture with SDN technology support: when the Base Station performs the functions of an SDN controller, and when air baluns are used as SDN controllers. This paper also compared the technologies of software-defined networking and virtualization of network functions. Practical relevance – this paper provides a detailed description of the structure and application possibilities of UAV networks, functional diagrams of SDN and NFV. The network architectures presented in this paper using SDN controllers in the flying (air balloon) and ground (Base stations) segment are examples of the implementation of SDN technology in a UAV network.of SDN and NFV technologies, and also considers the application of these technologies in UAV networks.

Named Data Networking is a novel network architecture for the future internet in which data is named rather than addressed. This enables routers to forward data more efficiently based on its name, outperforming IP‐based routing. Another networking architecture that divides the control plane and the data plane is software‐defined networking (SDN). In an SDN network, the control plane decides on routing while the data plane forwards data packets. This separation increases routing flexibility and scalability. Numerous techniques to improve routing can be achieved with NDN and SDN integration. This paper provides an in‐depth examination of routing approaches in NDN based on SDN, emphasizing design principles, algorithms, and performance measures. We begin by summarizing the NDN architecture and delving into its essential components. We next go into the core routing ideas in NDN and categorize and study several routing solutions based on Software Defined Networks. Finally, we highlight the need for scalable, effective, and secure routing systems that may satisfy the expanding requirements of the contemporary internet. We also suggest open research topics in NDN routing based on SDN. This review provides an extensive overview of current centralized routing approaches in NDN, including their limitations and future possibilities.

This paper proposes a Traffic Engineering (TE) scheme for routing in a target network topology that supports OpenFlow using a metric called congestion contribution and Software-Defined Networking (SDN). According to the network traffic volume has been increasing dramatically, it is clear that the congestion, which is caused by the imbalance of the traffic flow, can be occurred. Furthermore, an Interior Gateway Protocol (IGP) that is used for an Autonomous System (AS) also can cause congestion because it uses snapshot information of network topology to make the shortest path-based routing table. Due to the reasons mentioned above, much research on TE has been conducting to enhance the network performance. However, as an example of TE, the Multi-Protocol Label Switching (MPLS) using Resource Reservation Protocol (RSVP) is not flexible in reconfiguring the labeled path, and it has resource overhead that cannot be ignored. Thus, we propose a TE scheme using SDN to overcome the limitations in the target network. In order to show the feasibility of the proposed scheme, TE application predicts the bandwidth utilization for network devices in SDN networks. It also determines the near-optimized routing path for the maximization of the average bandwidth utilization with avoiding the congestion nodes. Our scheme can configure network devices (e.g., SDN switches) along the paths of traffic flows so that the SDN network can have maximum bandwidth utilization. Through experiments, it is shown that our proposed scheme can utilize the average bandwidth of the whole topology up to 66%.

Distributed Denial of Service (DDoS) attacks represent a major threat to network security.. In response, this paper examines the potential of countering DDoS attacks through the integration of Software-Defined Networking (SDN). SDN separates the network control logic from the underlying routers and switches, which facilitates the communication between software components. Moreover, the synergy of SDN with Machine Learning (ML) and Deep Learning (DL) technologies provide a promising approach for effective threat mitigation. This systematic review explores the evolving landscape of information security defense frameworks within the context of Internet of Things (IoTs) security. Over the past five years, numerous articles have contributed to the understanding of SDN based DDoS defense architecture. This review encompasses various aspects, including the design of SDN based DDoS frameworks, implementation steps, data analysis methods, DDoS data sources, and application scenarios of defense frameworks. Performance and characteristics of different defense technologies are analyzed, addressing common challenges in the research field. This study provides a valuable reference for researchers to develop efficient and reliable DDoS defense frameworks in SDN mode

Efficient network monitoring is an important basis work for network management. Generally, many management applications require accurate and timely statistics about network states at different aggregation levels at low cost, such as malicious traffic detection, traffic engineering, etc. Moreover, the network environment to be monitored is constantly changing and expanding, including not only the data center for cloud computing but also the Internet of Things (IoT) for smart urban sensing, which requires the intensive study of more fine-grained network monitoring. As is well known, the development of efficient network monitoring approaches greatly relies on a flexible monitoring framework. Software defined network (SDN) can provide dramatic advantages for network management by separating the control plane and data plane. Therefore, it is a good choice to design a flexible monitoring framework based on the advantages of SDN. However, most research works only take advantage of the centralized control feature in SDN, which leads to limited improvement in the flexibility of the monitoring framework. This paper proposes a flexible monitoring framework named FlexMonitor, which can realize greater flexibility based on not only the centralized control feature, but also the high programmability in the controller and the limited programmability in the openflow switches in SDN. There are two key parts in FlexMonitor, namely the monitoring strategy deployment part and the monitoring data collection part, which can enrich the deployment methods of monitoring strategies and increase the kinds of monitoring data sources, respectively. Based on the NetMagic platform, this monitoring framework was implemented and evaluated through realizing a distributed denial of service (DDoS) detection approach. The experimental results show that the proposed DDoS detection approach has a better detection performance compared with other related approaches as well as indirectly show that FlexMonitor can flexibly support a variety of efficient monitoring approaches.

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 a revolutionary paradigm that has transformed the way networks are designed, deployed, and managed. By decoupling the control plane from the data plane, SDN provides centralized control and programmability, offering numerous advantages over traditional networking approaches. This paper provides a comprehensive review of SDN concepts and applications, focusing on key aspects such as architecture, fundamentals, challenges, and future directions. We begin with an introduction to SDN, discussing its definition, evolution, and relevance in modern networking. We then delve into the fundamentals of SDN, exploring its key components, architecture, and control plane functionality. Next, we examine various SDN applications, including network management, traffic engineering, security, and cloud computing. We also discuss the challenges facing SDN, such as scalability, security, and interoperability, and highlight emerging trends and future research directions in the field. Through this review, we aim to provide a comprehensive understanding of SDN's impact on modern networking and its potential for future innovation.

In Wireless Sensor Network (WSN), resource optimization is one of the challenging issues. It improves the network performance. This can be achieved with the help of a new networking technology, called Software Defined Networking (SDN). The concept of SDN is to decouple the control plane from the forwarding plane of a network to improve the performance of the networking applications. Traffic Engineering (TE) is a domain of network applications that manages the network traffic, and try to optimize the network resources. SDN offers multiple features to support TE applications. SDN combined with WSN forms a new networking domain called Software Defined WSN (SDWSN). Since WSN has resource-constrained nodes, resource optimization is a need in WSN. This paper proposes an optimized link utilization based approach for TE in SDWSN. The proposed approach discusses about a traffic optimization algorithm, called Optimized Link Allocation based TE (OLA-TE). OLA-TE is implemented at the controller node and helps to improve the overall network utilization. The SDN sensor nodes find the optimized path for traffic routing with the help of controller node. The proposed approach is compared with the traditional WSN approach. IBM CPLEX Optimization Studio and MATLAB are used for validation purposes. The CPLEX results assure a network flow-cost optimization of about 10%. The results of MATLAB show that despite having extra control traffic on network, the network performance gets improved over the period of time.

The proliferation of mobile demands and increasingly multifarious services pose challenges to both the radio access networks (RANs) and the core network (CN). Unfortunately, the traditional networks can hardly meet the increasing requirements. Software-defined network (SDN) and network virtualization (NV) are considered as innovative paradigms to stress these problems. By extending SDN to wireless networks, software-defined wireless network (SDWN) consisting of both RANs and CN can be obtained. In this paper, the RANs and CN of SDWN are virtualized to virtual access points (VAPs) and network slices respectively based on NV technology. Considering the system scenario that a SDWN consists of overlapping RANs, an optimal joint VAP and network slice selection scheme is proposed. To characterize the complicated transmission performance of VAPs and network slices, the concepts of utility function and network calculus are introduced, and the joint utility of VAPs and network slices is formulated based on which the optimal VAP and network slice combination corresponding to the maximal joint utility can be selected. The simulation results demonstrate the efficiency of the proposed scheme.

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

In this paper, we describe the integration of Software Defined Networking (SDN) in the mobile backhaul as a disruptive approach to streamline the transport network. In this work we leverage SDN to optimize the mobile backhaul transport by removing all mobile specific tunnelling and replace it with more efficient MPLS or Carrier Grade Ethernet deployed either over electrical or optical networks. The paper also presents the testbed with complete end to end system including off the shelf base stations, SDN enabled mobile backhaul switches and virtualize network elements (i.e. Mobility Management Entity (MME), Serving/Packet Gateway (S/P-GW)) running on the cloud. This testbed is currently accepted as European Telecommunication Standards (ETSI) Proof of Concept and the results are used to describe the benefits for operators and end users. Moreover, an initial design of services based on the proposed virtualized mobile network architecture is proposed. The results of the testbed show the benefits for mobile operators in terms of Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) savings but more importantly the development of services that benefit from optimal usage of resources.

Software defined network (SDN) can effectively improve the performance of traffic engineering and will be widely used in backbone networks. Therefore, new energy-saving schemes must take SDN into consideration; this action is extremely important owing to the rapidly increasing energy consumption in telecom and Internet service provider (ISP) networks. Meanwhile, the introduction of SDN in current networks must be incremental in most cases, for technical and economic reasons. During this period, operators must manage hybrid networks in which SDN and traditional protocols coexist. In this study, we investigate the energy-efficient traffic engineering problem in hybrid SDN/Internet protocol (IP) networks. First, we formulate the mathematical optimization model considering the SDN/IP hybrid routing mode. The problem is NP-hard; therefore, we propose a fast heuristic algorithm named hybrid energy-aware traffic engineering (HEATE) as a solution. In our proposed HEATE algorithm, the IP routers perform shortest-path routing by using distributed open shortest path first (OSPF) link weight optimization. The SDNs perform multi-path routing with traffic-flow splitting managed by the global SDN controller. The HEATE algorithm determines the optimal setting for the OSPF link weight and the splitting ratio of SDNs. Thus, the traffic flow is aggregated onto partial links, and the underutilized links can be turned off to save energy. Based on computer simulation results, we demonstrate that our algorithm achieves a significant improvement in energy efficiency in hybrid SDN/IP networks.

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.