Software Defined Networking Technology Research Articles

Game theory-based switch migration strategy for satellite networks

Software-Defined Networking (SDN) separates the control plane and data plane of the network, abstracting the underlying infrastructure of applications and network services and handing it over to the SDN controller for unified management. The introduction of SDN technology into satellite networks effectively addresses control challenges posed by dynamic topologies and intermittent inter-satellite links. Due to factors such as geography and usage patterns, the distribution of remote sensing data traffic is uneven in both space and time. Some remote sensing satellites may acquire large volumes of data within a short period and transmit them back to the ground through intermediate nodes, leading to a sudden surge in load on certain controllers. This situation adversely impacts the performance of remote sensing data transmission. To mitigate this, switch migration operations are required to alleviate the load on overloaded controllers and to achieve the load balance among all controllers. In this paper, a switch migration scheme based on game theory is proposed under a three-layer satellite network architecture, which is designed based on the management costs and ensures timely triggering of the migration operations. Moreover, a game theory-based matching optimization model is formulated in the scheme to address the matching problem between target controllers and migration candidate switches. We further propose a distributed algorithm to seek the Nash equilibrium within the game. Experimental results demonstrate that, compared to other relevant algorithms, this approach can find the near-optimal matching strategy for each target controller and reduce the average latency of these controllers.

MADRLOM: A Computation offloading mechanism for software-defined cloud-edge computing power network

Cloud-edge computing power network often exhibits complex and heterogeneous structures, posing several challenges to computation offloading that significantly impact network performance and the efficient utilization of computation resources. In this paper, we propose a cloud-edge computing power network architecture that efficiently integrates cloud and edge computing resources into a single network system using software-defined networking technology to support upper-layer business applications. In this context, existing computation offloading methods struggle with issues like users' personalized requirements for latency and energy consumption, as well as the inability to adapt to dynamically complex environments. To overcome these challenges, we introduce a computation offloading mechanism, MADRLOM, focusing on the network's heterogeneity and modeling the computation offloading problem at the cloud-edge end. We formalize the offloading problem as a Markov process and employ a multi-agent deep reinforcement learning algorithm based on priority experience replay sampling to address the planning problem of computation offloading, allowing user equipment to continuously optimize offloading strategies in response to environmental changes and achieve rational resource allocation. Through dynamic offloading strategies, computation tasks can be intelligently allocated to appropriate computing nodes, thereby achieving optimal resource utilization. We utilized the Mininet simulation platform to construct the experimental environment for the software-defined cloud-edge computing power network and compared it with several representative computation offloading strategies. The experimental results demonstrate that the MADRLOM significantly reduces the total system overhead in the software-defined cloud-edge computing network and shows excellent adaptability to environmental changes.

An IoT-based low-cost architecture for smart libraries using SDN

In the evolving landscape of smart libraries, this research pioneers an IoT-based low-cost architecture utilizing Software-Defined Networking (SDN). The increasing demand for more efficient and economical solutions in library management, particularly in the realm of RFID-based processes such as authentication, property circulation, and book loans, underscores the significance of this study. Leveraging the collaborative potential of IoT and SDN technologies, our proposed system introduces a fresh perspective to tackle these challenges and advance intelligent library management. In response to the evolving landscape of smart libraries, our research presents an Internet of Things (IoT)-based low-cost architecture utilizing SDN. The exploration of this architectural paradigm arises from a recognized gap in the existing literature, pointing towards the necessity for more efficient and cost-effective solutions in managing library processes. Our proposed algorithm integrates IoT and SDN technologies to intelligently oversee various library activities, specifically targeting RFID-based processes such as authentication, property circulation management, and book loan management. The system's architecture, encompasses components like the data center, SDN controllers, RFID tags, tag readers, and other network sensors. By leveraging the synergy between RFID and SDN, our innovative approach reduces the need for constant operator supervision in libraries. The scalability and software-oriented nature of the architecture cater to extensive library environments. Our study includes a two-phase investigation, combining practical implementation in a small-scale library with a simulation environment using MATLAB 2021. This research not only fills a crucial gap in current knowledge but also lays the foundation for future advancements in the integration of IoT and SDN technologies for intelligent library management.

S2DN: Design of robust authentication protocol with session key establishment in multi-controller based software-defined VANETs

Advancements in technology and the adoption of innovative developments have significantly simplified our lives. One notable innovation in the networking domain is Software Defined Networking (SDN), which revolutionizes the network layer by enabling centralized network administration and re-programmability. SDN technology finds application in diverse fields such as Vehicular Ad-hoc Networks (VANETs), Wireless Sensor Networks (WSNs), Internet of Things (IoT) communications, and cloud-fog computing. The integration of SDN technology into VANET systems has notably improved network performance but has also raised security concerns. To tackle this issue, this paper focuses on devising an authentication mechanism to facilitate secure communication across various VANET levels by establishing a shared session key. Moreover, the research proposes a method to mitigate the challenge of single points of failure, a typically difficult issue to address. To ensure the security of all confidential information during protocol execution, simulations are conducted using the Scyther tool. Additionally, an informal security analysis is performed, demonstrating the robustness of the proposed protocol. The proposed protocol also performs outstanding in terms of performance results

Application of SDN-IP hybrid network multicast architecture in Commercial Aerospace Data Center

Abstract The increasing amount of massive data generated by commercial spacecraft in orbit puts forward higher and higher requirements for the stability, reliability, and computing power of computer systems for commercial aerospace data center. Data center computer systems are gradually transforming from X86 architecture and IP network model to a platform model with cloud computing and software-defined network (SDN) technology. This article proposes a new network architecture based on a unicast/multicast protocol for data interaction between the SDN and IP network. There are three main contributions of this article. The first is that the architecture proposed in this article aims to reduce end-to-end transmission latency and packet loss. The second is to improve the flexibility of system configuration and precise control when the SND controller state changes. The third is to verify the feasibility of deploying network architecture in a real data center environment.

An ICN-Based On-Path Computing Resource Scheduling Architecture with User Preference Awareness for Computing Network

The Computing Network is an emerging network paradigm that aims to realize computing resource scheduling through the intrinsic capabilities of the network. However, existing resource scheduling architectures based on conventional TCP/IP networks for the Computing Network suffer from deficiencies in routing flexibility and a lack of user preference awareness, while Information-Centric Networking (ICN) holds the potential to address these issues. ICN inherently supports dynamic routing in scenarios such as multi-homing and mobility due to its routing mechanism that is based on content names rather than host addresses, and it is further enhanced by the integration with Software-Defined Networking (SDN) technologies, which facilitate convenient network-layer route readdressing, thus offering a conducive environment for flexible routing scheduling. Furthermore, ICN introduces novel routing protocols that, compared with the more rigid protocol designs in conventional TCP/IP networks, offer greater flexibility in field usage. This flexibility allows for the incorporation of customized fields, such as “preference”, enabling the perception of user preferences within the network. Therefore, this paper proposes a novel ICN-based on-path computing resource scheduling architecture named IPCRSA. Within this architecture, an original design for computing resource request packet format is developed based on the IPv6 extension header. Additionally, preference-based computing resource scheduling strategies are incorporated, which employ the technique for order preference by similarity to ideal solution (TOPSIS) combined with the entropy weight method, to comprehensively evaluate computing resource nodes and use a roulette-selection algorithm to accomplish the probability selection of destination nodes. Experimental results indicate that, in comparison to alternative scheduling schemes, IPCRSA exhibits significant advantages in enhancing scheduling flexibility, improving scheduling success rates, and catering to diverse user requirements.

A Comparative Study on Software-Defined Network with Traditional Networks

The emergence of a technological age, in which nearly everything is linked and accessible from anywhere, is largely due to the Internet. Hence, traditional Networks continue to be difficult and complex to operate despite their widespread deployment. As a consequence, it is challenging to set up the networks in accordance with the established protocols and respond to variations in load and defects by reconfiguring the structure. The data and control layers are packaged together in the contemporary systems, further complicating problems by vertically integrating them. Because of their limited resources and unsecured transmission channel, wireless sensor networks (WSNs) are particularly vulnerable to persistent security attacks. Many hundreds of self-organized and resource-constrained sensor nodes make up the WSN. It is possible to build an ad-hoc network of sensors without a specified architecture or centralised control because these sensor nodes are often organised in a scattered form. The underlying problem is to strengthen the privacy enforcement in these systems because WSNs will have power over real-time applications, where unauthorized conduct might lead to significant harm. In order to address this issue, the software-defined network (SDN) technology was developed. With the creation of SD, network operators now have more legitimacy and influence over the networks. Based on a global perspective and centralised management of the network topology, SDN has increased the rigour of its enhancing security. This study describes the software-defined network, outlining its fundamental ideas, how it differs from conventional networking, and its architectural tenets. Also, it highlighted the key benefits of SDN while emphasizing availability, maliciousness, packet delivery and duplication ratio, and efficiency.

A Comprehensive Survey of Distributed Denial of Service Detection and Mitigation Technologies in Software-Defined Network

The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible one. However, due to its inherent architectural flaws, SDN still faces new security threats. This paper expounds on the architecture and security of SDN, analyzes the vulnerabilities of SDN architecture, and introduces common distributed denial of service (DDoS) attacks within the SDN architecture. This article also provides a review of the relevant literature on DDoS attack detection and mitigation in the current SDN environment based on the technologies used, including statistical analysis, machine learning, policy-based, and moving target defense techniques. The advantages and disadvantages of these technologies, in terms of deployment difficulty, accuracy, and other factors, are analyzed. Finally, this study summarizes the SDN experimental environment and DDoS attack traffic generators and datasets of the reviewed literature and the limitations of current defense methods and suggests potential future research directions.

Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

The advent of Industry 5.0, driven by cutting-edge 6G technologies such as immersive cloud eXtended Reality (XR), Autonomous vehicles, holographic communication, and Digital Twin (DT), is set to trigger a substantial upswing in the deployment of the Industrial Internet of Things (IIoT) devices interconnected within networks. This expansion of IIoT devices will create a wider attack surface and increase the risk of data breaches, privacy violations, and system disruptions. Therefore, it is essential to design innovative mechanisms to ensure the reliability and security of these advanced 6G applications, as well as the IIoT devices that support them. In this context, we propose a novel Software-defined Networking (SDN)-based Ensemble Learning (EL) Framework for Secure IIoT applications in 6G and beyond, called AdaptSDN. The proposed framework leverages SDN technology to dynamically allocate network resources and deploy security measures on demand. It also leverages EL techniques to improve the intrusion detection system’s accuracy. By isolating IIoT devices into network slices, the framework limits the impact of attacks and reduces the potential for cascading failures. Digital twins are used for creating a virtual replica of the IIoT network, allowing for a real-time security threat detection. In particular, AdaptSDN includes three main modules: (1) A novel digital Twin (DT)-enabled data gathering and selection of informative features module to achieve two main objectives: reducing computational complexity and improving detection performance; (2) An SDN-based lightweight adaptive boosting module that uses an advanced boosting EL techniques to dynamically adjust weights and to effectively identify and respond to IIoT attacks in real-time; and (3) A zero-touch resources provisioning module that employs a non-cooperative game theory approach. This approach allows for automatically provisioning various resources in a network to efficiently mitigate network attacks; it enables SDN nodes to obtain the required virtual resources, i.e., storage, computing, and bandwidth, from the main orchestrator with respect to IIoT attack type. We carried out comprehensive experiments to assess the effectiveness of our proposed framework in detecting real-world IIoT attacks. The numerical results confirm that AdaptSDN has the potential to enable secure and reliable IIoT applications in 6G and beyond, meeting the stringent service requirements of the new emerging applications.

МЕТОДИ ТА ЗАСОБИ ЗАХИСТУ РЕСУРСІВ В КОМП’ЮТЕРНІЙ SDN-МЕРЕЖІ

The work is dedicated to the analysis and improvement of methods and tools for building the architecture of software-defined networks (SDNs). A crucial aspect involves comparing the differences between network management using traditional methods and utilizing an SDN controller. Primary attention is given to the development of security models based on software-defined networks.The research is grounded in the analysis of specific cases involving the use of such networks, including gathering opinions and expert assessments from professionals in the field. It leverages publicly available information on methods and tools for securing the architecture of software-defined networks. SDN technology provides greater flexibility and speed in implementing security measures, allowing real-time responses to threats. In contemporary conditions, where cyber threats are becoming more complex and pronounced, SDN networks enable the detection of attacks, blocking malicious actions, and applying security policies in real-time. However, the increasing complexity of cyber threats and constant technological changes necessitate further development and enhancement of the information security of SDN network resources for companies. There is a need for further analysis and improvement of methods and tools for protecting information and other resources in computer-based SDN networks. The necessity for further analysis and improvement of methods and tools for protecting information and other resources in computer-based SDN networks serves as the foundation for this research. As a result, an enhancement in the level of information security for SDN network resources has been achieved by: 1) reducing the intervention time in the network; 2) applying an improved method of traffic processing based on network security of packet flows, allowing desired network applications to efficiently manage forwarding.

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems.

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

Software-Defined Networking-Based Campus Networks Via Deep Reinforcement Learning Algorithms: The Case of University of Technology

As a consequence of the COVID-19 pandemic, networks need to be adopted to satisfy the new situation. People have been introduced to new modes of working from home, attending teleconferences, and taking part in e-learning. Other technologies, including smart cities, the Internet of Things, and simulation tools, have also seen a rise in demand. In the new situation, the network most affected is the campus network. Fortunately, a powerful and flexible network model called the software-defined network (SDN) is currently being standardized. SDN can significantly improve the performance of campus networks. Consequently, many scholars and experts have focused on enhancing campus networks via SDN technology.
 Integrating deep reinforcement learning (DRL) with SDN is pivotal for advancing the quality of service (QoS) of contemporary networks. Their integration enables real-time collaboration, intelligent decision making, and optimized traffic flow and resource allocation.
 The system proposed in this research is a DRL algorithm applied to a campus network—the University of Technology—and investigated as a case study. The proposed system employs a two-method approach for optimizing the QoS of a network. First, the system classifies service types and directs TCP traffic by using a deep Q-network (DQN) for intelligent routing; then, UDP traffic is managed using the Dijkstra algorithm for shortest-path selection. This hybrid model leverages the strengths of machine learning and classical algorithms to ensure efficient resource allocation and high-quality data transmission. The system combines the adaptability of DQN with the proven reliability of the Dijkstra algorithm to enhance dynamically the network performance.
 The proposed hybrid system, which used DQN for TCP traffic and the Dijkstra algorithm for UDP traffic, was benchmarked against two other algorithms. The first algorithm was an advanced version of the Dijkstra algorithm that was designed specifically for this study. The second algorithm involved a Q-learning (QL)-based approach. The evaluation metrics included throughput and latency. Tests were conducted under various topologies and load conditions.
 The research findings revealed a clear advantage of the hybrid system in complex network topologies under heavy-load conditions. The throughput of the proposed system was 30% higher than the advanced Dijkstra and QL algorithms. The latency benefits were pronounced, with a 50% improvement over the competing algorithms.

Mobility management enhancement in smart cities using software defined networks

Achieving sustainability in cities relies on effective mobility management (MM) that serves current and future generations. It involves establishing an inclusive transportation system to address many issues, like traffic congestion, air pollution, and greenhouse gas emissions. Beyond environmental concerns, robust mobility management has social and economic advantages, fostering improved access to vital services like healthcare, education, and employment. Software-defined networking (SDN) presents a viable solution for enhancing MM within networks. Unlike traditional setups, SDN merges MM through a programmable control plane, streamlining network configurations and enabling features like handover, load balancing, and quality of service (QoS). The utilization of SDN technology extends to various facets of sustainable city networks, encompassing areas like network security, performance optimization, big data processing, energy efficiency, emergency management, carbon emissions reduction, intelligent services, and MM in vehicular networks. Despite the advantages of SDN-based mobility management, it's crucial to acknowledge the challenges and limitations posed by traditional MM methods that SDN aims to overcome. The paper explores SDN's potential in sustainable cities, focusing on how it can transform mobile device management, support various networking technologies, and evaluate the impact of SDN methods on existing MM systems, considering factors like scalability and compatibility. The paper asserts that SDN-based MM has substantial potential for promoting sustainable urban development. By centralizing control, adapting to changing conditions, and optimizing resource allocation, SDN can contribute to reduced energy consumption, lower carbon emissions, and more efficient urban mobility. It emphasizes the importance of addressing potential drawbacks to ensure successful implementation in sustainable cities.

Security and risk analysis in the cloud with software defined networking architecture

Cloud computing has emerged as the actual trend in business information technology service models, since it provides processing that is both cost-effective and scalable. Enterprise networks are adopting software-defined networking (SDN) for network management flexibility and lower operating costs. Information technology (IT) services for enterprises tend to use both technologies. Yet, the effects of cloud computing and software defined networking on business network security are unclear. This study addresses this crucial issue. In a business network that uses both technologies, we start by looking at security, namely distributed denial-of-service (DDoS) attack defensive methods. SDN technology may help organizations protect against DDoS assaults provided the defensive architecture is structured appropriately. To mitigate DDoS attacks, we offer a highly configurable network monitoring and flexible control framework. We present a dataset shift-resistant graphic model-based attack detection system for the new architecture. The simulation findings demonstrate that our architecture can efficiently meet the security concerns of the new network paradigm and that our attack detection system can report numerous threats using real-world network data.

SDN Integration with Firewalls and Enhancing Security Monitoring on Firewalls

Software-defined Networking (SDN) has revolutionized the way networks are managed and operated by decoupling the control plane from the data plane. This separation allows for centralized control and programmability, offering greater flexibility, scalability, and agility in network management.Firewalls, renowned for their robust security features, play a critical role in protecting network traffic. Integrating SDN principles and technologies with Firewalls presents an opportunity to enhance their management, scalability, and orchestration capabilities. This paper explores the integration of SDN with Firewalls, focusing on leveraging SDN controllers and software defined networking architectures to augment the underlying BSD-based operating system. By integrating SDN controllers, organizations can centrally manage firewall policies, dynamically enforce security rules, and gain real-time visibility into network traffic. Furthermore, SDN enables efficient scalability of Firewalls by dynamically allocating resources and load balancing traffic. This paper also explores methods to enhance security monitoring and analytics on Firewalls. It focuses on leveraging advanced techniques, technologies, and integration approaches to optimize security monitoring and strengthen the firewall’s ability to detect and respond to security threats. Security monitoring and analytics are crucial components of modern network infrastructure to detect and mitigate potential threats. Firewalls, known for their robust security features, serve as critical gateways for network traffic. Enhancing security monitoring and analytics capabilities on Firewalls can significantly improve threat detection, incident response, and overall network security posture. Index Terms—Firewalls , Software Defined Networking(SDN) , Enhancing

Network-Compute Co-Optimization for Service Chaining in Cloud-Edge-Radio 5G Networks

Flexible resource management can be facilitated by recent drastic advances in software-defined networking (SDN) and network function virtualization (NFV) technologies. Most of recent studies on resource management have mainly focused on the independent optimization of network function and computing function on top of a single-layer SDN architecture. In this paper, we focus on networking and processing resource orchestration to enhance the performance of network/computing functions in a hierarchical cloud-edge-radio 5G network architecture. Specifically, we propose a dynamic resource management algorithm, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RACER</i> , to process network/computing functions by opportunistically exploiting a tradeoff between the characteristics of the cloud and edge with respect to the amount of available resources and transmission delay. Herein, we decompose an original long-term problem including complex decisions in all network layers (that is, cloud, edge, and radio) into spatio-temporal subproblems without loss of optimality by leveraging Lyapunov optimization. Then, we derive the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RACER</i> algorithm to find the decisions in each layer and each time slot. This online-fashioned algorithm is pragmatic because it not only exploits information obtained from instantaneous time slots, but also operates in a distributed manner with small message exchanges between the different layers. Moreover, we demonstrate the excellent performance improvement of the proposed dynamic and holistic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RACER</i> algorithm compared to static and independent policies in the 5G network environment.

SUSIC: A Secure User Access Control Mechanism for SDN-Enabled IIoT and Cyber–Physical Systems

The integration of thriving Information and Communications Technology (ICT) and Cyber Physical Systems (CPS) has spawned several innovative applications such as remote healthcare, smart and intelligent transportation, smart logistics, smart grids, public safety etc. An emerging Software Defined Networks (SDN) technology further enabled to optimize the communication among industrial IoT (IIoT) and CPS entities. Nonetheless, the communication on public channel among different IIoT entities in an SDN-enabled environment may be exposed to various security threats due to wireless and insecure communication channels. To counter these security challenges in the way of wider CPS or IIoT adoption, we propose a novel three-factor authenticated key exchange mechanism (SUSIC) for SDN-enabled IIoT ecosystem. The SUSIC enables a registered user to access real-time data from physical IIoT environment directly after having mutual authentication performed through SDN-enabled controller node. The scheme is proved to be secure under rigorous formal and informal security analysis. Moreover, the simulation results and performance evaluation signifies towards achieving better trade-off between security functionalities and computational overheads comparatively.

SFC-based multi-domain service customization and deployment

In order to satisfy diversified service demands, future networks are expected to provide higher quality network services, especially in 5G scenarios. Network Function Virtualization (NFV) is a promising architecture to achieve flexible service provisioning by introducing Service Function Chain (SFC) technology. With explosive expansion of network devices and end users, the complex multi-domain network environment and personalized service demands of different users pose serious challenges to efficient service provisioning. To this end, this paper presents a novel SFC-based Multi-domain Service Customization and Deployment framework, MSCD, by blending merits of Software Defined Networking (SDN) and NFV technologies. MSCD framework can automatically customize multi-domain network services based on users’ personalized service preferences, and deploy them in multi-domain networks, to provide customizable multi-domain services for different users. Taking into account user personalized service preferences, resource allocation and VNF dependency constraints, the multi-domain service customization and deployment problem is mathematically formulated as an optimization model with the target of service satisfaction degree maximization. Furthermore, a Greedy strategy based Heuristic Service Customization and dePloyment (GHSCP) algorithm is proposed to customize and deploy the multi-domain services efficiently. Simulation results demonstrate that the proposed GHSCP algorithm is efficient and outperforms comparison algorithms in terms of service acceptance ratio and service satisfaction degree.

Exploring Path Computation Techniques in Software-Defined Networking: A Review and Performance Evaluation of Centralized, Distributed, and Hybrid Approaches

Software-Defined Networking (SDN) is a networking paradigm that allows network administrators to dynamically manage network traffic flows and optimize network performance. One of the key benefits of SDN is the ability to compute and direct traffic along efficient paths through the network. In recent years, researchers have proposed various SDN-based path computation techniques to improve network performance and reduce congestion.&#x0D; This review paper provides a comprehensive overview of SDN-based path computation techniques, including both centralized and distributed approaches. We discuss the advantages and limitations of each approach and provide a critical analysis of the existing literature. In particular, we focus on recent advances in SDN-based path computation techniques, including Dynamic Shortest Path (DSP), Distributed Flow-Aware Path Computation (DFAPC), and Hybrid Path Computation (HPC).&#x0D; We evaluate three SDN-based path computation algorithms: centralized, distributed, and hybrid, focusing on optimal path determination for network nodes. Test scenarios with random graph simulations are used to compare their performance. The centralized algorithm employs global network knowledge, the distributed algorithm relies on local information, and the hybrid approach combines both. Experimental results demonstrate the hybrid algorithm's superiority in minimizing path costs, striking a balance between optimization and efficiency. The centralized algorithm ranks second, while the distributed algorithm incurs higher costs due to limited local knowledge. This research offers insights into efficient path computation and informs future SDN advancements.&#x0D; We also discuss the challenges associated with implementing SDN-based path computation techniques, including scalability, security, and interoperability. Furthermore, we highlight the potential applications of SDN-based path computation techniques in various domains, including data center networks, wireless networks, and the Internet of Things (IoT).&#x0D; Finally, we conclude that SDN-based path computation techniques have the potential to significantly improvement in-order to improve network performance and reduce congestion. However, further research is needed to evaluate the effectiveness of these techniques under different network conditions and traffic patterns. With the rapid growth of SDN technology, we expect to see continued development and refinement of SDN-based path computation techniques in the future.

Assessment of SDN Controllers in Wireless Environment Using a Multi-Criteria Technique

Software-defined network (SDN) technology can offer wireless networks the advantages of simplified control and network management. This SDN subdomain technology is called the software-defined wireless network (SDWN). In this study, the performance of four controllers in an SDWN environment is assessed, since the controller is the most significant component of the entire network. Using the Mininet-WiFi platform, the performance of each controller is evaluated in terms of throughput, latency, jitter, and packet loss. Moreover, a multi-criteria evaluation is introduced and applied to provide a fair comparison between SDWNs. This study provides an appropriate configuration of SDWNs that is useful for network engineering and can be used for SDWNs performance optimization.