DQR: An Efficient Deep Q-Based Routing Approach in Multi-Controller Software Defined WAN (SD-WAN)

Controller-proxy: Scaling network management for large-scale SDN networks

Artificial intelligence based load balancing in SDN: A comprehensive survey

Software defined network (SDN) is a promising technology which can reduce network management complexity through the decoupling of the control plane and data plane. Due to large number of switches in the data plane, distributed and multiple controllers are necessary in the control plane for managing the switches. The switch controller mapping strategy for identifying the mapping relationships between the switch and controller is crucial in order to optimize the network performance. Considering the dynamics of the network behavior, it is quite important and challenging to develop models to reflect the network topology dynamics and to propose method for solving the long-term network performance optimization. Inspired by the recent advances in Artificial Intelligence (AI), in this paper, we propose a Deep Reinforcement Learning (DRL) based strategy for solving the switch controller mapping problem. A DRL based mapping strategy is proposed, in which Markov Decision Process (MDP) formulation is devised and Deep $Q$ -network (DQN) is proposed to achieve the maximization of long-term system performance by leveraging network latency, load balancing and system stability. Extensive simulations show that the DQN based algorithm can achieve the best system stability results while maintaining moderate switch controller latency and system equilibrium performance comparing with the optimization which only considers current system performance for switch controller mapping decision, and the optimization approaches which generate mapping decisions purely based on latency or load balancing separately.

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.

This paper explores the Quality of Service (QoS) performance of two widely used Software-Defined Networking (SDN) controllers, POX and Ryu, using Mininet for network simulation. SDN, a transformative approach to network architecture, separates the control and data planes, enabling centralized management, improved agility, and cost-effective solutions. The study evaluates key QoS parameters, including throughput, delay, and jitter, to understand the capabilities and limitations of the POX and Ryu controllers in handling traffic under diverse network topologies. The research employs a systematic methodology involving the design of custom network topologies, implementation of OpenFlow rules, and analysis of controller behavior under simulated conditions. Results reveal that while POX offers simplicity and ease of use, making it suitable for smaller-scale applications and experimentation, Ryu provides superior scalability and adaptability for more complex network environments. The findings highlight the strengths and challenges of each controller, providing valuable insights for organizations seeking to optimize SDN deployment. This study contributes to the growing body of knowledge on SDN technologies and their role in building scalable, efficient, and resilient network infrastructures.

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.

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.

Software Defined Networking (SDN) is believed to be a promising networking practice in facing the challenges of ever-increasing traffic demand and network growth. With wider deployment of SDN enabled network infrastructures, the conventional network virtualization problem will gradually evolve into an SDN virtualization problem, in which both virtual data plane and control plane entities are instantiated from their physical network counterparts. In this paper, we first argue that the delay constrained virtual SDN embedding problem is in general different from the conventional virtual network (VN) embedding problem. This paper tries to define the delay constrained vSDN embedding problem space, followed by a potential network architecture that supports SDN virtualization. This study then formulates the delay constrained vSDN embedding problem with the objective of maximizing request acceptance ratio and proposes a set of heuristics to tackle the embedding problem. Our results show that both the control plane placement and data plane mapping are restricted by a strict controller-to-switch (C2S) delay constraint. When the delay constraint is relaxed over a certain value, the acceptance ratio is dominated by control plane resources or data plane resources. Certainly, with different resource configurations and substrate network diameters, the tolerances of C2S delay constraint are distinct.

International audience

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.

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

Software Defined Networking (SDN) has a potential to address a number of networking challenges, but still presents other financial and business challenges such as budget constraints, acceptability and trust amongst network operators. A hybrid SDN deployment is a conceivable solution, because it offers the benefits of SDN and traditional network capabilities, including budget constraints adaptability, falling back on time-tested traditional tools, central network programmability and will offer an organization a smooth transition to SDN. As modern-day communication has become an imperative feature of day to day business functions, there is a need to balance the network load to achieve improved network performance, minimizing delay and reduce the time taken for network response. In this paper, we develop and study multi-path load balancing in a hybrid SDN data plane. The load balancer’s performance is tested using four different hybrid SDN topologies, namely, Fat Tree, Ring, Mesh and Torus topology with the OpenDayLight controller. The main contribution of this work is a data plane congestion and link overloading load balancing testing and analysis for deployment (before or after network failure) in hybrid SDN environments. The obtained results display an improved overall network performance and reduced network delay.

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.

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.