Traditional Software Defined Networking Research Articles

Reducing the Risk of Cyber Attack in SDN Network by using Blockchain

In the contemporary digital landscape, Software-Defined Networking (SDN) has emerged as a transformative approach that decouples network control from hardware, enabling greater flexibility and centralized management. However, this innovation has also introduced new vulnerabilities, making SDN networks susceptible to cyber-attacks. To reduce the risk of cyber-attack blockchain can play vital role. In paper we propose a novel framework that leverages blockchain technology to enhance the security and resilience of SDN environments against potential threats. By integrating blockchain’s decentralized and immutable characteristics, our approach facilitates secure data transactions, enhances network visibility, and fosters trust among network participants. We present a comprehensive analysis of the vulnerabilities inherent in traditional SDN architectures and outline method, how blockchain can mitigate these risks through secure authentication, data integrity, and enhanced access controls.Furthermore, we conduct a series of experiments to evaluate the performance impact and security benefits of our proposed solution. The results demonstrate a significant reduction in the likelihood of cyber-attacks, showcasing the viability of blockchain as a potent tool for safeguarding SDN networks. Our findings underscore the importance of interdisciplinary approaches in addressing the evolving challenges of network security, paving the way for more resilient and secure SDN infrastructures in the future.

Performance enhancement in hybrid SDN using advanced deep learning with multi‐objective optimization frameworks under heterogeneous environments

SummaryThe growth of software‐defined networking (SDN) enhances network strength and provides flexible routing, especially in heterogeneous environments. Hence, an efficient framework is required for recent networks. Recently, hybrid SDN with the restricted deployment of SDN switches has been integrated with a conventional network that provides improved communication performance compared to traditional SDN systems. However, the recent hybrid SDNs lack effective link protection and optimal routing when used with complex topologies. Hence, this study presents a novel deep learning–based hybridized multi‐stacked autoencoder with the duo‐directed gated recurrent unit (MSAE‐DDGRU) for automatic link failure prediction in hybrid SDN. Moreover, a multi‐objective zebra optimizer (MO‐ZeO) is introduced to perform optimal routing by solving multiple routing constraints. The developed study is processed with the Python platform, and publicly available GEANT topology is utilized for the whole experimental process. Various assessment measures like accuracy, precision, sensitivity, packet loss, cost, maximum link utilization (MLU), policy violation rates (PVRs), packet delivery ratio (PDR), and delay are analyzed and compared with existing studies. The developed technique achieved an accuracy of 96%, precision of 92%, sensitivity of 93%, PDR of 99.4%, PVR of 0.0005, and delay of 1.2 s are obtained.

Controller placement with critical switch aware in software-defined network (CPCSA).

Software-defined networking (SDN) is a networking architecture with improved efficiency achieved by moving networking decisions from the data plane to provide them critically at the control plane. In a traditional SDN, typically, a single controller is used. However, the complexity of modern networks due to their size and high traffic volume with varied quality of service requirements have introduced high control message communications overhead on the controller. Similarly, the solution found using multiple distributed controllers brings forth the 'controller placement problem' (CPP). Incorporating switch roles in the CPP modelling during network partitioning for controller placement has not been adequately considered by any existing CPP techniques. This article proposes the controller placement algorithm with network partition based on critical switch awareness (CPCSA). CPCSA identifies critical switch in the software defined wide area network (SDWAN) and then partition the network based on the criticality. Subsequently, a controller is assigned to each partition to improve control messages communication overhead, loss, throughput, and flow setup delay. The CPSCSA experimented with real network topologies obtained from the Internet Topology Zoo. Results show that CPCSA has achieved an aggregate reduction in the controller's overhead by 73%, loss by 51%, and latency by 16% while improving throughput by 16% compared to the benchmark algorithms.

Ensuring reliable network operations and maintenance: The role of PMRF for switch maintenance and upgrades in SDN

In the realm of software-defined networking (SDN), ensuring robust maintenance operations and the consistent performance of switch operations, especially during maintenance and upgrades, is of paramount importance. Traditional SDN methods, both reactive and proactive, face challenges like extended convergence times, inefficient use of Ternary Content-Addressable Memory (TCAM) space, and unpredictable maintenance time. To tackle these issues and enhance maintenance operations, this paper introduces the Preventive Maintenance Routing Framework (PMRF) – a novel approach designed for uninterrupted upgrades in SDN settings. Our framework focuses on selecting reliable backup paths and timely isolation of the maintenance switch except critical switch, framed as an NP-hard mixed-integer linear programming problem. To address this complexity, we present a multi-objective heuristic algorithm that efficiently determines the best path for upcoming affected flows. Testing shows that, in varied network conditions, PMRF markedly reduced packet drops, potentially enhancing SDN operation efficiency, minimizing end-to-end delays, and maintaining failure recovery times below 50 ms and efficiently manage TCAM resources. In summary, PMRF offers a robust solution for optimizing network reliability and efficiency during maintenance operations and upgrades in core networks.

A Graph reinforcement learning based SDN routing path selection for optimizing long-term revenue

Software-Defined Network (SDN) paradigm decouples control plane from data plane and provides a logically-centralized control to whole underlying network, which enables the controller to centrally configure the network, and has demonstrated great potential in fully utilizing network capacity. However, the traditional SDN does not consider the accumulative long-term revenue in routing configuration for network service provider (SP). For example, it is prone to choosing the path passing through important nodes, which may increase the possibility of future congestion, and hamper the SP’s long-term revenue. Deep reinforcement learning (DRL) has brought about the transformative advances in decision-making when facing uncertainty. As a well-known type of DRL, Deep Q-network (DQN) can explore optimal routing schemes from high dimensional network state. However, most of existing DRL based methods fail to provide satisfactory configuration when confronted with a topology unseen during training. Meanwhile, many existing network optimization schemes ignore the impact of nodes and global network attributes on the routing configuration. Consequently, they may perform poorly in real scenarios. In this paper, we propose a graph reinforcement learning (GRL) based SDN routing selection scheme, GN-DQN for optimizing SP’s accumulative long-term revenue. First, besides the link embeddings, GN-DQN explicitly learns the representations of the routers and globe network topology with full graph neural network block. Then, through utilizing the formed network representation as the state, DQN is used to learn to sequentially choose the optimal routing path from the candidate path set to maximize the accumulative long-term revenue. Thorough experiments on multiple real network topologies show that our proposed GN-DQN outperforms other GRL based and traditional routing selection schemes, and can generalize over arbitrary topologies. Moreover, GN-DQN also demonstrates great robustness to the link failure scenario.

Comparison of software defined networking with traditional networking using NS2 simulator

The Software Defined Network (SDN) is an emerging concept in network engineering that entails the decoupling of the control plane and data plane. The data plane is executed on discrete nodes, while a centralised control plane is accountable for overseeing network-wide functionalities. In conventional networks, the integrated planes are incorporated into the network suite, and the transmission is determined by the route. In this work, the traditional way of routing using a communication plane is compared with routing using an SDN controller. The present study employs the NS2 simulator to evaluate the quality of service performance under high background traffic conditions. In the event of network congestion, it is imperative to utilize the available bandwidth to its fullest extent in order to increase the rate of bit transmission per second. The results of the traditional network and SDN network based on various parameters like Ambiguous collision, Delay, Jitter, Availability and Latency are compared in this paper. The results show that the SDN network's performance is better with concerning time.

A Comprehensive Survey on Knowledge-Defined Networking

Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

Detection and Mitigation of (D)DoS Attacks in SDN Environment Using Entropy

Software Defined Networking (SDN) is a paradigm for the networks, where the control planes and data planes are separated. It provides centralized network control by separating the network’s control logic from the underlying hardware devices. However, like traditional networks SDN is also susceptible to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. This paper aims to detect and mitigate DoS and DDoS attacks in an SDN environment using an entropy-based approach. The proposed mechanism calculates the entropy of the network over the collected traffic, and derives a dynamic threshold according to the network traffic conditions to determine whether the environment is subject to DoS or DDoS attacks. In the event of the attack, the proposed mechanism installs a drop flow rule into underlying forwarding devices, discarding the traffic sent from attacking host to victim host

AI-assisted traffic matrix prediction using GA-enabled deep ensemble learning for hybrid SDN

A hybrid software-defined network (SDN), which is a network where traditional routers and SDN protocols coexist during the incremental deployment of SDNs, requires real-time link traffic information for effective deployment. This has called for the need of accurate real-time data analytics and traffic prediction methods. To date, various traffic prediction frameworks have been studied to facilitate analysis and extraction of valuable information from huge sets of incomplete and noisy data. However, due to the linear nature of network design, mainly characterized by manual control plane forwarding configurations, existing traffic prediction frameworks cannot perform consistent traffic prediction over multiple datasets in modern dynamic networks. To address this issue, ensemble-driven approaches based on deep learning (DL) have recently been suggested as a promising solution. Nevertheless, determining the most appropriate combination of baseline DL architectures to be adopted for accurate traffic prediction remains a challenge. This paper proposes a novel DL framework for improved traffic prediction in hybrid SDNs. The framework combines a deep ensemble learning model utilizing multiple dimensionality reduction algorithms and a genetic algorithm (GA). The multi-objective GA is used to perform dynamic optimization of the connection weights and thresholds of the deep ensemble learning model while overcoming the local optima problem. Experimental results show that the proposed approach can achieve more accurate forecast of link traffic than the traditional baseline DL frameworks.

Distributed Controller Placement in Software-Defined Networks with Consistency and Interoperability Problems

Software-defined networking (SDN) brings an innovative approach to networking by adopting a flow-centric model and removing networking decisions from the data plane to provide them centrally from the control plane. A single centralized controller is used in a traditional SDN design. However, the complexity of modern networks, due to their size and requirements’ coarseness, has made using a single controller a source of performance bottlenecks. Similarly, the solution found by using multiple controllers in distributed control planes brings forth the profound issue of interoperability, consistency, and the “controller placement problem” (CPP). It is an NP-hard problem that deals with positioning controllers at optimum locations within the network and mapping with resources at the data plane to meet quality of service (QoS) requirements. Over the years, the problem has received significant attention from the research community, and many solutions have been considered. This paper offers an in-depth review of the proposals by providing an updated evolution of the problem concerning the application environment, design objectives, and cost and controller type. Based on our findings, new research ideas were identified and discussed.

Digital Twin-Based Zero-Touch Management for IoT

The rapid development of the Internet of Things (IoT) requires network automation, to improve management efficiency and reduce manual operations. Zero-touch network is a promising technology for empowering network management automation by creating virtualized networks for software-based solutions. However, the traditional software-defined network (SDN) technology is not suitable for IoT devices, due to its massive, heterogeneous, and distributed characteristics. In this paper, we introduce digital twin technology (DT) into the IoT, and propose a DT modeling method through ontology and knowledge graph technologies, which maps IoT elements in the digital space and provides the advantages of centralized control, device abstraction, and flexible control of management. Then, referring to the conceptual architecture of a zero-touch network, a DT-based zero-touch management framework suitable for IoT is established. Finally, aiming at specific device management and network optimization problems in the IoT, a zero-touch management scheme with digital twin technology as the core and intention as the driver is proposed, and the effectiveness of the proposed method is demonstrated using an example.

Latency control in service chaining using P4-based data plane programmability

Service chaining is becoming one of the most considered service deployment frameworks in the context of Network Function Virtualization (NFV) in edge and data center environments, conveniently supported by automatic connectivity configurations offered by Software Defined Networking (SDN). Current research on the topic is focusing on how to guarantee Quality of Service (QoS) in terms of guaranteed end-to-end latency for time critical services. Indeed, latency issues may depend on intra-server virtualization inefficiencies, leading to Virtual Network Function (VNF) delivery delays, or by congestion events occurring at intermediate network elements connecting VNFs. Latency control requires stateful information such as flow delay measurements at a per-packet level, typically not available at traditional SDN switches or inside the VNF. This paper proposes the adoption of SDN data plane programmability exploiting the P4 language and presents two P4 pipeline solutions, suitable for both intra-rack and inter-rack service chain deployments, to automatically check the path latency experienced by selected high priority flows, also resorting to the recent in-band telemetry applications. The programmable pipelines enforce proactive in-network functions, such as priority change or drop actions, in order to guarantee a bound SFC segment latency delivery, including both the network and the segment VNFs. The proposed solutions are implemented and evaluated in a network testbed employing programmable software switches showing their effectiveness in guaranteeing the configured end-to-end latency, and the limited effort in terms of additional processing at the P4 switch. The evaluation is carried out using the reference P4 software switch, i.e., BMv2. The aim is to validate the full P4 capabilities and the code feasibility in terms of scalability, load and resource impact and added intra-switch latency. The experimental results show the proposed approach scales with the number of forwarded flows and achieves per-segment latency control enforcement in both congested and non-congested scenarios with a very limited impact on the switch extra-latency, exploiting finer per-packet tuning of drop and priority change simply applicable through flow entry configuration. Applicability analysis on hardware switches guaranteeing line rate performance are provided.

Mathematical model for forwarding packets in communication network

The software-defined networking (SDN) represents the future backbone of the Internet. In general, to understand the performance and the limitation of SDN is a prerequisite for deploying these networks and making the most of their resources. For achieving that goal, this paper discusses the difference between traditional networks and SDN over wide area network (WAN) where an accurate queuing model of packet forwarding is proposed to measure the performance of many factors affecting the system, such as response time, service rate, utilization, throughput and delay through a vEdge router. Additionally, it focusses for studying algebraic estimates of the probability distribution of the system states. The Matrix-Geometric solution procedure of a phase-type distribution queue with first-in, first-out (FIFO) discipline is used. The analysis results indicate that the proposed queuing model gives a more accurate approximation for software-defined WAN (SD-WAN) controller system performance than the present, where this solution shows that the most important results are benefits of centralized management of SD-WAN, an increase in bandwidth through separating the control plan from the data plan, thus reducing volume operations in the data plan, a delay and an increase in packet transfer speed. Additionally, it improves business performance and diminishes budget.

Enhanced Security of Software-defined Network and Network Slice Through Hybrid Quantum Key Distribution Protocol

Software-defined networking (SDN) has revolutionized the world of technology as networks have become more flexible, dynamic and programmable. The ability to conduct network slicing in 5G networks is one of the most crucial features of SDN implementation. Although network programming provides new security solutions of traditional networks, SDN and network slicing also have security issues, an important one being the weaknesses related to openflow channel between the data plane and controller as the network can be attacked via the openflow channel and exploit communications with the control plane. Our work proposes a solution to provide adequate security for openflow messages through using a hybrid key consisting of classical and quantum key distribution protocols to provide double security depending on the computational complexity and physical properties of quantum. To achieve this goal, the hybrid key used with transport layer security protocol to provide confidentiality, integrity and quantum authentication to secure openflow channel. We experimentally based on the SDN-testbed and network slicing to show the workflow of exchanging quantum and classical keys between the control plane and data plane and our results showed the effectiveness of the hybrid key to enhance the security of the transport layer security protocol. Thereby achieving adequate security for openflow channel against classical and quantum computer attacks.

A Software-Defined Architecture for Integrating Heterogeneous Space and Ground Networks

In recent years, various types of heterogeneous networks develop rapidly. The integration of multi-type networks have great values in the fields of military and civil applications. The challenges of integrating multiple networks covers the heterogeneity of multiple aspects, e.g., the architectures, protocols, and switching mechanisms. The existing interconnection technologies of heterogeneous networks mainly include traditional static protocol gateways, traditional software-defined network (SDN) gateways, and improved SDN gateways. However, traditional static protocol gateways need to be customed in advance according to specific scenarios, which leads to the lack of flexibility. Traditional SDN gateways are often used for connecting homogeneous networks. The existing improved SDN gateways often neglect the efficiency and cost of integrating heterogeneous networks. In our work, we propose a software-defined architecture for integrating heterogeneous space and ground networks (SD-SGN). First, we propose an integrated architecture that utilizes SDN gateways and southbound interfaces to shield subnets’ heterogeneity ranging from the physical layer to the network layer. Second, we use the multi-class multi-level flow tables to provide a flexible data plane. Third, we offer an efficient control plane based on the subnet abstraction and global collaborative optimization. Fourth, we give a further discussion on customizing a complete network service based on the proposed SDN architecture. Last, extensive simulations demonstrate that this SDN architecture is effective and performs well in terms of costs, efficiency, and performance.

Convergence Time Monitoring Algorithm in Hybrid Software Defined Networks

Network consummation especially dependent on traffic monitoring. Therefore, Software Defined Network (SDN) technology is submitted to support the flow control suitable monitoring by providing a global view of the network. Unfortunately, replacing the entire traditional network to SDN is complex, which leads to the need of SDN switches deployment to the current network. Thus, a hybrid network environment has emerged which consists of centralized controller, SDN switch and legacy routers. Hence, the advantage of the integration of traditional network and SDN will take place. The controller can collect SDN data instantly, while it waits for a long time to obtain the legacy network data. On the other hand, the rest of paths cannot be processed directly by the controller. Therefore, legacy path load data is estimated for the past time to support the controller for obtaining the current data. The convergence time of the proposed algorithm takes more convergence time than the full SDN by only 12%. Therefore, the proposed algorithm provides installing the minimum possible number of SDN switches that reduce the infrastructure cost.

A review on P4-Programmable data planes: Architecture, research efforts, and future directions

Software Defined Networking (SDN) is a promising technology that provides flexibility, programmability, and network automation by shifting the network intelligence to the logically centralized controller. In SDN architecture, the controller maintains the global view of network topology; therefore the network management is efficient as compared to the traditional networks. Moreover, the cost of SDN devices is less due to the use of open source network operating system instead of proprietary and vendor-specific software. In spite of the flexibility offered by the SDN, OpenFlow enabled switches have a fixed behavior as specified in the datasheet of switch ASIC. They recognize fixed set of header fields according to the support of OpenFlow version. In addition, SDN is suffering from scalability and performance issues because SDN switches heavily dependent on the control plane to forward the packets which increases the data-control communication overhead. To resolve these issues, P4-Programmable data plane switches can be used. The analysis of review articles about SDN suggests insufficient focus on the data plane programmability. Therefore, this paper provides the comprehensive overview of domain-specific language (P4) for the programmability of the data plane. We have discussed the problems in the traditional SDN architecture and then, how these problems can be managed by the data plane programmability. Further, this study critically analyze the research articles based on the P4 programming language for network traffic monitoring, DDoS attack detection, load balancing, and packet aggregation and disaggregation. Finally, we have identified the research gaps and highlighted the open research challenges in the field of data plane programmability for the future directions.

Aloe: Fault-Tolerant Network Management and Orchestration Framework for IoT Applications

Internet of Things (IoT) platforms use a large number of low-cost resource constrained devices and generates millions of short-flows. In-network processing is gaining popularity day by day to handle IoT applications and services. However, traditional software-defined networking (SDN) based management systems are not suitable to handle the plug and play nature of such systems. In this paper, we propose Aloe, an auto-scalable SDN orchestration framework. Aloe exploits in-network processing framework by using multiple lightweight controller instances in place of service grade SDN controller applications. The proposed framework ensures the availability and significant reduction in flow-setup delay by deploying instances in the vicinity the resource constraint IoT devices dynamically. Aloe supports fault-tolerance with recovery from network partitioning by employing self-stabilizing placement of migration capable controller instances. Aloe also provides resource reservation for micro-controllers so that they can ensure the quality of services (QoS). The performance of the proposed system is measured by using an in-house testbed along with a large scale deployment in Amazon Web services (AWS) cloud platform. The experimental results from these two testbeds show significant improvement in response time for standard IoT based services. This improvement of performance is due to the reduction in flow-setup time. We found that Aloe can improve flow-setup time by around 10%-30% in comparison to one of the states of the art orchestration framework.

ID-Based SDN for the Internet of Things

The rapid development of the Internet of Things (IoT) has made impressive achievements, raising a heated discussion about IoT big data, in which data security and privacy issues are key concerns. Due to the ubiquity of IoT, IoT big data has not only brought convenience to people's daily lives, but also increased the potential attack surfaces for cybercriminals. At the same time, considering the characteristics of resource constraints and heterogeneity, with traditional network security solutions it can be difficult to achieve ideal results in the IoT environment, which further exacerbates the challenges faced by IoT big data security. In this case, the advantages introduced by software defined networking (SDN) have the potential to meet the challenges of IoT security risks. To this aim, we propose an ID-based SDN secure network architecture called IBSDN. Different from the traditional SDN solution, IBSDN is committed to providing IoT with endogenous trusted services on the network side by embedding unforgeable terminal identities in the data stream. This network-level trusted service can prevent IoT terminals from consuming restricted resources for the sake of security, providing greater scalability and manageability for network security monitoring.

Evaluation of Firewall and Load balance in Fat-Tree Topology Based on Floodlight Controller

Today it has become important to reconfigure the networks in to new form to be more manageable, scalable, dynamic and programmable. The networks recently are so inflexible and failing to deal with the required changes for the Information Technology. Software Defined Networking (SDN) is a modern paradigm that focused to change the main idea of current network infrastructure (traditional network) by breaking the chain between the data forwarding and the control planes to introduce flexible programmability network. This paper makes comparison between the performance of traditional fat-tree network and SDN fat-tree network, which found that average Round Tripe Time (RTT) in SDN fat-tree topology will decrease by 8.96% than traditional fat-tree topology. Then shows the basic operation of OpenFlow protocol that can be applied on fat-tree topology by using SDN technology and how that can be effect on the performance of network and make it more flexible to enable the SDN module applications, like load balancer and firewall for optimizing the SDN network. In this paper the physical switches are replaced by software switches in a virtual network environment and display the SDN structure in GUI, also Floodlight controller is chosen to use as the network operating system for SDN network.