Delay constrained vSDN embedding in WAN

Data center operators are accepting software defined networking (SDN) to manage their networks, but it remains challenging how to provide desirable virtual SDN services to tenants in a public cloud. We design SVirt, which enables highly flexible virtual SDN in a multi-tenant cloud by a substrate-agnostic SDN virtualization architecture. By redesigning the physical switch's processing pipeline with a late-binding key extractor, SVirt supports virtual SDN switches with different processing pipelines simultaneously on a physical switch. In the control plane, SVirt enables many-to-one and one-to-many mapping when allocating the physical resource for a virtual network, which embraces arbitrary topology and TCAM resource demanded by a virtual network. In the data plane, SVirt explicitly carries the forwarding context information in the packets, overcoming the context-loss problem in a virtual SDN network. We develop a NetFPGA prototype of SVirt switch. Evaluations based on event-driven simulations and prototype-based experiments demonstrate that, compared with traditional approaches, SVirt significantly enhances the cloud's capability to accept various virtual SDN requests and improves the network's throughput.

In a network virtualization-enabled software-defined networking (SDN), the problem of virtual network embedding (VNE) is a major concern. Although a number of VNE algorithms have been proposed, they fail to consider the efficient utilization of substrate resources or the network load extensively, thus resulting in less efficient utilization of substrate resources or higher blocking ratio of the virtual networks. In this paper, we study the problem of mapping a number of virtual networks in SDN and formulate the VNE problem as a network load minimization problem. Since the formulated optimization problem is NP-hard and it cannot be solved conveniently, we propose a two-stage VNE algorithm consisting of node mapping stage and link mapping stage. Numerical results demonstrate that the effectiveness of our proposed algorithm.

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.

SummaryNetwork virtualization (NV) technologies have attracted a lot of attention as an essential solution for future networking infrastructure. The NV enables multiple tenants to share the same physical infrastructure and to create independent virtual networks (VNs) by decoupling the physical network in terms of topology, address, and control functions. One feasible way to realize full NV involves considering solutions based on the software‐defined networking (SDN) paradigm using its programmability. The SDN contributes many benefits to both network operations and management including programmability, agility, elasticity, and flexibility. There are several SDN‐based NV solutions; however, they suffered from a lack of scalability, high availability. Also, they have high latency between control and data plane because of proxy‐based architecture. In this thesis, we introduce a new NV platform, named Open Network Hypervisor (ONVisor). The design objectives include, among the features, (1) multitenancy, (2) scalability, (3) flexibility, (4) isolated VNs, and (5) VN federation. ONVisor was designed and implemented by extending Open Network Operating System, an open‐source SDN controller. The main features of ONVisor are (1) isolated control and data plane per VN, (2) support of distributed operations, (3) extensible translators, (4) on‐platform VN application development and execution, and (5) support of heterogenous SDN data‐plane implementations. Several experiments are conducted on various test scenarios in different test environments in terms of control and data plane performance compared to nonvirtualized SDN network. The results show that ONVisor can provide VNs a little bit lower control plane performance and similar data plane performance.

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 an emerging architecture which decouples networks control plane and data plane physically. It makes control plane programmable trough a centralized controller, and builds intelligent and flexible networks. The OpenFlow is one of the most famous SDN protocols, which acts as a southbound interface between control plane and data plane. In this survey, SDN implementation approaches and different southbound interfaces, beside different version of OpenFlow, are introduced. In addition to general architecture of SDN, different wireless architectures are discussed. Here, also potential SDN’s applications and research areas including hot topics such as Information Centric Networks, Cloud and datacenters, multimedia, wireless and mobile networks over SDN are reviewed.

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.

Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane.

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

Network virtualization promises a future Internet that inherently supports technological advancement. Extensive literature studies have been devoted to the fundamental problem, namely Virtual Network Embedding (VNE) in network virtualization, which instantiates the virtual network request (VNR) customized by the Service Provider (SP) to the substrate network managed by the Infrastructure Provider (InP). Different from prior studies, in this work, we identify security issues that are associated with the VNE process, which is further incorporated and addressed in the Security-Aware Virtual Network Embedding (SVNE) problem. Our efforts aim to fill the major gaps in the literature studies on the VNE problem: first, we assess the security issues in VNE at node, topology and network levels and explicitly define open, flexible and fine-granular security plans that can address above issues; second, we present and evaluate a comprehensive framework that implements the proposed security plans and address the SVNE problem.

Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Hidden Markov Model (HMM) scheme is proposed for cluster management. The utilization of the bandwidth is an issue because of the extensibility of the control plane. The Control plane in SDN focuses on a centralized solution, where a single control entity has an overall view of the network. To meet the administration, the board necessities of large-scale network situations, the controls plane is regularly completed in the sort of distributed controller clusters, Cluster management technology monitors all sort of events and should keep up a reliable global network status, which usually leads to big data in SDNs. Cluster management technology monitors all types of events and must maintain a consistent global network status, which usually leads to big data in SDNs. Simultaneously, the cluster security is an open issue because of the programmable and dynamic features of SDNs.

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) enhances the flexibility and programmability of networks by separating control plane and data plane. The logically centralized control mechanism makes the control plane vulnerable in both single and multiple controller scenarios. Malicious third parties can exploit vulnerabilities of reactive forwarding mode to launch distributed denial-of-service (DDoS) attacks against SDN controllers. Unfortunately, existing DoS/DDoS solutions under single controller can not afford effective performance under multiple controllers due to the absence of cooperative detection and mitigation. To solve the above problem, we propose a blockchain-based SDN-targeted DDoS defense framework (BSD-Guard) that can provide cooperative detection and mitigation mechanism to protect SDN controllers. BSD-Guard introduces a blockchain-based secure middle plane between control plane and data plane. The secure middle plane calculates the suspect rate of new flows based on the collected packets’ information and reports suspect lists to blockchain for immutably storing and sharing. Besides, the smart contract deployed on blockchain in advance constitutes collaborative defense strategies based on the suspect lists reported from multiple SDN domains. When receiving defense strategies, the secure middle plane converts them to specific flow table actions and installs actions into relevant switches. The experimental results indicate that BSD-Guard can efficiently detect DoS/DDoS attacks in multiple controllers scenario and issue precise defensive strategies near the source of attack by identifying the attack path.

Artificial intelligence based load balancing in SDN: A comprehensive survey

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.