Software Defined Networking Framework Research Articles

T‐RESIN: Throughput‐aware dynamic resource orchestration for Internet of Everything‐enabled software‐defined edge networks

SummaryIn this work, we address the problem of resource allocation in Internet of Everything (IoE)‐enabled software‐defined edge networks. In the existing literature, the researchers considered optimizing the performance of the software‐defined networking (SDN) platform using a single‐tier architecture, where the Internet of Things (IoT) devices are in the same tier. However, with the advent of edge computing, we can explore the two‐tier architecture of edge networks—local tier and edge tier—in the presence of SDN, which has not been explored. Hence, we propose an evolutionary game‐based resource allocation scheme for software‐defined edge networks. Additionally, we aim to optimize the QoS of the edge‐based IoE services while optimizing the throughput of the system. The IoT devices use the proposed scheme in the local tier to identify the optimized mapping to the SDN switches. On the other hand, in the edge tier, the proposed scheme aims to optimize the throughput while allocating the IoE service to the optimal subset of edge devices. We evaluated the performance of the proposed scheme using the Python3‐based Mininet platform in the presence of Ryu Controller and Open vSwitches. Ryu is an element‐based software defined networking framework, which offers software elements with well‐described API that enables developers to produce new network control functions. We observed that by using T‐RESIN, the network throughput increases by 27.98%–31.84% than using the existing schemes.

DDoS Attack Detection in SDN-Based Instrumented Ad Hoc Networks

The physical characteristics of today's urban intersection structures and the traffic flows caused by unplanned road intersections lead to many negative effects such as time/cash loss, stress, increased fuel consumption, and more. For this reason, many studies are being conducted on traffic management systems, an application of smart cities, in both academic and commercial circles. In recent years, it has been observed that the VANET (Vehicular Ad Hoc Networks) architecture, which easily enables communication between vehicles or with devices on the side of the field, thus transporting relevant traffic data to the center, is frequently used in these studies. When Software Defined Networking emerged as a new technology, it brought many advantages such as high availability, scalability, and performance, but also introduced new security vulnerabilities targeted by attackers. This research primarily focuses on a resource-based detection approach by combining the powers of Software Defined Networking and s-Flow-RT technology against Distributed Denial of Service Attacks. In the simulation study conducted within the scope of this work, an SDN (Software Defined Networking)-based DDoS (Distributed Denial of Service) attack was carried out, and changes in data before and after the attack were examined. Traffic for the DDoS attack was generated with the Hping3 application. The RYU controller (a component-based software-defined networking framework) was selected as the software-defined network controller to create software-defined networks, and the Mininet emulator was used. A traditional computer network's Ubuntu virtual machine was used to carry out the attack in the scope of the work.

An integrated SDN framework for early detection of DDoS attacks in cloud computing

Cloud computing is a rapidly advancing technology with numerous benefits, such as increased availability, scalability, and flexibility. Relocating computing infrastructure to a network simplifies hardware and software resource monitoring in the cloud. Software-Defined Networking (SDN)-based cloud networking improves cloud infrastructure efficiency by dynamically allocating and utilizing network resources. While SDN cloud networks offer numerous advantages, they are vulnerable to Distributed Denial-of-Service (DDoS) attacks. DDoS attacks try to stop genuine users from using services and drain network resources to reduce performance or shut down services. However, early-stage detection of DDoS attack patterns in cloud environments remains challenging. Current methods detect DDoS at the SDN controller level, which is often time-consuming. We recommend focusing on SDN switches for early detection. Due to the large volume of data from diverse sources, we recommend traffic clustering and traffic anomalies prediction which is of DDoS attacks at each switch. Furthermore, to consolidate the data from multiple clusters, event correlation is performed to understand network behavior and detect coordinated attack activities. Many existing techniques stay behind for early detection and integration of multiple techniques to detect DDoS attack patterns. In this paper, we introduce a more efficient and effectively integrated SDN framework that addresses a gap in previous DDoS solutions. Our framework enables early and accurate detection of DDoS traffic patterns within SDN-based cloud environments. In this framework, we use Recursive Feature Elimination (RFE), Density Based Spatial Clustering (DBSCAN), time series techniques like Auto Regressive Integrated Moving Average (ARIMA), Lyapunov exponent, exponential smoothing filter, dynamic threshold, and lastly, Rule-based classifier. We have evaluated the proposed RDAER model on the CICDDoS 2019 dataset, that achieved an accuracy level of 99.92% and a fast detection time of 20 s, outperforming existing methods.

Quality of services in software defined networking: challenges and controller placement problems

<span>Quality of service (QoS) is pivotal for ensuring effective and reliable network performance, yet achieving end-to-end QoS within current network architectures remains a persistent challenge. The emergence of software defined networking (SDN) addresses limitations in traditional networking by offering a centralized control plane. This allows dynamic resource management and efficient enforcement of QoS policies by network administrators. However, the controller placement problem (CPP) within SDN poses a significant challenge, as identifying the optimal placement of controllers is a non-deterministic polynomial-time hardness (NP-hard) problem. Researchers are actively working on solutions to address this challenge, especially in large-scale networks where deploying controllers becomes complex. Additionally, maintaining QoS in terms of controller management presents another hurdle. This paper explores these challenges, delving into the literature and providing a comprehensive analysis of controller performance metrics related to QoS parameters such as load balancing, reliability, consistency, and scalability. By addressing these challenges, the research aims to enhance QoS within the SDN framework.</span>

Enhancing Network Intrusion Recovery in SDN with machine learning: an innovative approach

In modern network environments, the swift recovery of network flow intrusions poses a substantial challenge. Particularly in the context of Software-Defined Networks (SDN), addressing this challenge necessitates the strategic selection of backup paths based on traffic patterns. In response to this critical issue, our paper introduces a groundbreaking approach known as Machine Learning-based Network Intrusion Recovery (MLBNIR) for enhancing intrusion recovery in SDN. We leverage a dedicated SDN dataset to train a flow-based Machine Learning (ML) model, enabling a deeper understanding of traffic dynamics within the SDN framework. Our study, presented in this paper, reveals that the MLBNIR approach significantly reduces intrusion recovery time by up to 90% and concurrently increases network bandwidth consumption by up to 57% when compared to existing methods reviewed in the literature.

Real-World Multimedia Streaming for Software Defined Vehicular Ad Hoc Networks

Multimedia services with required Quality of Service (QoS) is one of the most critical challenges in Software Defined Network (SDN) based Vehicular Ad-Hoc Networks (VANETs). It forms an essential part of the Intelligent Transport System (ITS), where infotainment services play an essential role.Streaming multimedia is one of the most popular applications and has a high demand for VANET infotainment services. The major issues for multimedia streaming on VANET are scalability, mobility of vehicles, frequent connection failures, frequent change in network topology, and distributed architecture with heterogeneous devices. To overcome these problems and provide a better QoS, we propose using a hybridarchitecture with a combination of VANET and SDN called Software-Defined Vehicular Networks (SDVN). This work presents a modified POX controller-based SDN framework for VANETs, especially for multimedia streaming applications in realistic traffic patterns. The proposed work has a real-world setup developed using Simulation of Urban Mobility (SUMO), where iPerf generates multimedia traffic. Also, streaming standard-definition YouTube videos in real-time between the vehicular nodes was done. The modified POX controller could take advantage of the centralised perspective of the network for action determination, and the integrated spanning tree algorithm reduced the redundancy. Despite the dynamic nature of the testing environments, the proposed Modified POX Controller consistently outperformed VANET, with up to 21 to 42% better packet delivery ratio for higher data transfer rates. The overall improvement in QoS parameters also accompanies an improvement in the consumers Quality of Experience (QoE) factors.

A Novel Deep-Learning Model for Remote Driver Monitoring in SDN-Based Internet of Autonomous Vehicles Using 5G Technologies

The rapid advancement in the Internet of Things (IoT) and its integration with Artificial Intelligence (AI) techniques are expected to play a crucial role in future Intelligent Transportation Systems (ITS). Additionally, the continuous progress in the industry of autonomous vehicles will accelerate and increase their short adoption in smart cities to allow safe, sustainable and accessible trips for passengers in different public and private means of transportation. In this article, we investigate the adoption of 5G different technologies, mainly, the Software-Defined Networks (SDN) to support the communication requirements of delegation of control of level-2 autonomous vehicles to the Remote-Control Center (RCC) in terms of ultra-low delay and reliability. This delegation occurs upon the detection of a drowsy driver using our proposed deep-learning-based technique deployed at the edge to reduce the level of accidents and road congestion. The deep learning-based model was evaluated and produced higher accuracy, precision and recall when compared to other methods. The role of SDN is to implement network slicing to achieve the Quality of Service (QoS) level required in this emergency case. Decreasing the end-to-end delay required to provide feedback control signals back to the autonomous vehicle is the aim of deploying QoS support available in an SDN-based network. Feedback control signals are sent to remotely activate the stopping system or to switch the vehicle to direct teleoperation mode. The mininet-WiFi emulator is deployed to evaluate the performance of the proposed adaptive SDN framework, which is tailored to emulate radio access networks. Our simulation experiments conducted on realistic vehicular scenarios revealed significant improvement in terms of throughput and average Round-Trip Time (RTT).

LSSDNF: A Lightweight Secure Software Defined Network Framework for Future Internet in 5G–6G

As information technology advances quickly, so does the 5G–6G network management system, which is moving toward greater integration, decentralization, diversity, and intelligence. As flexibility is a crucial criterion for 5G–6G network architecture, we use the Software Defined Network (SDN) paradigm to make the programmability more flexible. Due to their ability to replace the current TCP/IP architecture with one that separates the control plane and data plane, software-defined networks have gained much popularity. However, they are susceptible to routing attacks. Therefore, this work proposes Lightweight Security Framework that combines blockchain technology with Software-Defined Networking (LSSDNF) to address this problem. The proposed framework adds the routing data that the controller withheld to the multichain blockchain. Here, a mininet network simulator is used to model the proposed framework. The data transfer rate or network throughput, bandwidth variation, and jitter have all been used to assess the performance of single-controller-SDN networks and multi-controller-SDN networks. The results demonstrate that the proposed framework performs better than the conventional single-controller-SDN architecture in terms of throughput, bandwidth fluctuation, and jitter.

LoRaWAN Network Downlink Routing Control Strategy Based on the SDN Framework and Improved ARIMA Model

In order to improve the downlink communication performance of the traditional LoRa wide area network (LoRaWAN), a LoRaWAN downlink routing control strategy based on the software defined networks (SDN) framework and the improved auto-regressive integrated moving average (ARIMA) model is proposed. The SDN architecture is used to monitor the network traffic, and the link bandwidth occupancy rate is calculated based on the monitored downlink traffic. Taking into account the impact of data volatility on the accuracy of the prediction results, the Savitzky–Golay (S–G) smoothing filter and the sliding window method are introduced for data pre-processing. Stationarity processing is carried out for the time series data in the window, and the ARIMA model is developed to predict the downlink bandwidth occupancy rate. The triangle module operator is then used to incorporate multiple path parameters to finally calculate the selectivity of different paths, and the optimal path for LoRaWAN downlink communication is then provided. Simulation and experimental results show that the root mean square error of the improved ARIMA prediction model is reduced by 87% compared with the standard ARIMA model. The proposed routing control strategy effectively reduces the service transmission delay and packet loss rate. In the LoRaWAN test environment, as the downlink load rate increases, the average link bandwidth occupancy rate of this solution increases by 12% compared with the traditional method.

Edge-Based Optimal Routing in SDN-Enabled Industrial Internet of Things

The efficiency of communication between devices in any network depends on the network architecture and the routing algorithms. This efficiency vastly affects the productivity of the Internet of Things (IoT) nodes when utilized in an industrial environment. The conundrum of using the conventional network architecture in an industry/factory connected by IoT devices has paved the way for the development of software-defined networking (SDN). Traditional network architectures lack network programmability and hinder the testing and performance evaluation of new communication protocols. The use of vendor-specific hardware is a major hindrance to create a network where the control is centralized for the deployment of different policies over the network. This article aims to find an optimal path for efficient communication between the nodes present in a programmable Industrial IoT (IIoT) network. A programmable IIoT network is created and end-user routing algorithms are designed to find the optimal path for communication. We integrate an SDN framework with IIoT nodes for the creation of a centralized programmable network architecture and deploy custom routing algorithms over it for evaluation. To further reduce the communication latency, we incorporate edge computing servers (ECSs) in the network. Three routing algorithms: 1) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means-based optimal path algorithm; 2) cluster-based optimal path algorithm; and 3) minimum interval-based optimal path algorithm, that can be deployed over an SDN are proposed in this article. Experimental results reveal that the proposed algorithms are more efficient than the existing algorithms.

MHSDN: A Hierarchical Software Defined Network Reliability Framework design

At present, attacks based on the vulnerability of the controller and flooding attacks still constitute a principal threat for hierarchical Software Defined Network (SDN), such as flow table tampering, malicious Application attacks, Distributed Denial of Service (DDoS) etc., due to the limitation against attacks based on known or unknown vulnerabilities for traditional cyber defence technology. Therefore, this study proposes an active defence architecture based on Mimic Defence (MD)–Mimic Hierarchical SDN Framework (MHSDN). Then endogenous security of MHSDN is theoretically analysed. Simultaneously, the attack surface measurement of MD is innovatively proposed, further improving the security and usability measurement standards of the MD system. Finally, to speed up detection and reduce defence cost of DDoS, this research proposes the Random Forest Feature Extract (RFFE) and tolerable switch migration. Simulation shows that RFFE has achieved a faster detection speed at the cost of less detection accuracy, and MHSDN can better improve the reliability of hierarchical SDN.

Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches

Massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) are two key services in fifth-generation (5G) mobile wireless networks. These networks have been developed with extremely high service quality requirements: scalability for mMTC and reliability with low latency for URLLC. Fifth-generation network slicing will play a key role in supporting the distinct requirements of various services. Software-defined networking (SDN), a promising technology for network softwarization, physically separates the network control plane from the data plane by centrally controlling switches with an SDN controller. However, control channel bottleneck and processing delays due to this centralized control may reduce the scalability, reliability, and security of SDN. This paper proposes an SDN framework with programming protocol–independent packet processor (P4) switches (SDNPS), and defines a packet format containing in-band network telemetry data to simultaneously support heavy Internet of Things and URLLC traffic in 5G network slices. The method both satisfies the requirements of mMTC and URLLC and alleviates the load on the SDN controller. P4 is an advanced switch interface technology that provides enhanced stateful forwarding and reveals a persistent state on the SDN data plane. To demonstrate the superiority of SDNPS, simulations are performed on conventional SDNs and SDNPS. SDNPS outperforms the other schemes in terms of average throughput, packet loss ratio, and packet delay for both the mMTC and URLLC network slices.

An end-to-end software-defined network framework and optimal service development model for SAGN

An end-to-end software-defined network framework and optimal service development model for SAGN

An improved Lagrangian relaxation algorithm based SDN framework for industrial internet hybrid service flow scheduling

The Industrial Internet is the key for Industry 4.0, and network control in the industrial internet usually requires high reliability and low latency. The industrial internet ubiquitously connects all relevant Internet of things (IoT) sensing and actuating devices, allowing for monitoring and control of multiple industrial systems. Unfortunately, guaranteeing very low end-to-end wait times is particularly challenging because the transmissions must be articulated in time. In the industrial internet, there usually coexist multiple streams. The amount of data for controlling business flows is small, while other business flows (e.g., interactive business flows, sensing business flows) typically transmit large amounts of data across the network. These data flows are mainly processed in traditional switches using a queue-based "store-and-forward" mode for data exchange, consuming much bandwidth and filling up the network buffers. This causes delays in the control flow. In our research, we propose an Software Defined Networking (SDN) framework to reduce such delays and ensure real-time delivery of mixed service flows. The scheduling policy is performed through the northbound Application Programming Interface (API) of the SDN controller so that the dynamic network topology can be satisfied. We use the concept of edge and intermediate switches, where each switch port sends data at a specific time to avoid queuing intermediate switches. We also introduce an improved Langerian relaxation algorithm to select the best path to ensure low latency. Finally, the path rules are deployed to the switches in flow tables through the SDN controller. Our mathematical analysis and simulation evaluation demonstrates that the proposed scheme is more efficient.

The Security of a Blockchain-Based File System in a Software Defined Network Framework

Developing distributed form of file security systems using Blockchain technology. Based on the idea of cloud storage as it is a leading storage technology for huge data storage. Blockchain is one of the trending technology for decentralized data storage systems that ensures privacy, confidentiality, data security, authentication, and integrity. As SDN network provides support to have various nodes in the network for the secure transaction of data from source to destination. Blockchain helps in keeping track of block data by constructing the gateway to make it immutable. BCFS refers to Blockchain-Based File System Security in SDN. In the designed system, a Web-Based Interface is developed an authorized entity can upload file data the user’s file is projected to encryption process and the block data is shared among the various nodes in the network. Along with Unique Document ID, encrypted random key, and hash data. This hash data value holds the file path and preserves in the blockchain into their corresponding block data folders. Detection of node failure across the network an automatic short path is chosen by the network and detection of an attack based on entropy value.

An SDN Framework for VANET

Vehicular Ad hoc Networks (VANETs) have become the area of research and development since last few years. Communication today is taking place in moving vehicles due to advancement in technology. In VANETs, Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication takes place. VANETs comprise of vehicles as nodes for the transfer of data in various situations. The routing protocols which were designed for many regular networks could not be applied to VANETs due to various unpredictable conditions. To communicate effectively through VANET, routing which has less delay and overhead is required. This paper gives a Software Defined Network (SDN) framework for efficient communication. Simulation results show that SDN framework improves the communication in terms of utilizing less delay. Index Terms: data transmission, routing algorithms, SDN, VANETs.

Dynamic Resource Allocation in Cloud Computing Based on Software-Defined Networking Framework

cloud computing has become more powerful with the inclusion of software-defined networking (SDN) in its environment. In Cloud Data Centers (CDCs), an important research issue is how to forecast and allocate resources efficiently whilst achieving Quality of Service (QoS) of users request with minimal overall power consumption; taking into account the frequent changes in resource requirements. In this paper, we propose a Supervisor Controller-based Software-Defined Cloud Data Center (SC-boSD-CDC) framework for dynamic resource allocation and prediction of cloud computing-based SDN. In this proposed module, Genetic Algorithm (GA) is proposed to deal with the multi-objective problem of dynamically forecasting the utilization of resources in both compute nodes and links bandwidth of network as well as energy consumption in the Cloud Data Center (CDC). Furthermore, a Virtual Machines (VMs) placement algorithm is also proposed to allocate computing resources and routing algorithms to choose the proper bandwidth links between switches; resulting in increased CPU and memory utilization and reduction in overall power consumption.

An All-wireless SDN Framework for BLE Mesh

The Internet of Things (IoT) paradigm combines the interconnection of massive amounts of battery-constrained and low-computational-power devices with low-latency and high-reliability network requirements. Additionally, diverse end-to-end services and applications with different Quality of Service (QoS) requirements are expected to coexist in the same network infrastructure. Software-defined Networking (SDN) is a paradigm designed to solve these problems, but its implementation in wireless networks and especially in the resource-constrained IoT systems is extremely challenging and has seen very limited adoption, since it requires isolation of data and control plane information flows and a reliable and scalable control plane. In this work, Bluetooth Low Energy (BLE) mesh is introduced as an adequate technology for an all-wireless SDN-BLE implementation, which is a technology that has become the de-facto standard for IoT. The proposed SDN-BLE framework uses a routing network slice for the data plane information flow and a flooding network slice for the control plane information flow, ensuring their isolation while still being transmitted over the wireless medium. The design and implementation of all the classical SDN layers on a hybrid BLE mesh testbed is given, where the data plane is formed by the BLE nodes and the control plane can be centralized on a server or distributed over several WiFi gateways. Several controllers are described and implemented, allowing the framework to obtain end-to-end network knowledge to manage individual nodes over the air and configure their behavior to meet application requirements. An experimental characterization of the SDN-BLE framework is given, where the impact of the different parameters of the system on the network reliability, overhead, and energy consumption is studied. Additionally, the distributed versus centralized control plane operation modes are experimentally characterized, and it is shown that the distributed approach can provide the same performance as the centralized one when careful system design is performed. Finally, a proof of concept for the SDN-BLE framework is presented, where a network congestion is automatically detected and the nodes responsible of such congestion are identified and reconfigured over the air, bypassing the congested links, to resume regular network performance.

Experimental Assessments and Analysis of an SDN Framework to Integrate Mobility Management in Industrial Wireless Sensor Networks

Industrial wireless sensor networks (IWSN) are expected to support in the near future a wide range of applications in which mobile nodes, for example, robots or humans, interoperate between them and with machinery. However, the real-time constraints imposed by the industrial processing make mobility support quite challenging. This article proposes an approach based on software-defined networking (SDN), herein named forwarding and time-slotted channel hopping scheduling over SDN (FTS-SDN), that handles transmission scheduling and node mobility in IWSN providing bounded end-to-end delays. The article presents the detailed design of the FTS-SDN and the implementation on commercial-off-the-shelf devices. In addition, the FTS-SDN performance are extensively discussed, providing simulation results and experimental measurements obtained in a real scenario.

From model to implementation: a network algorithm programming language

Software-defined networking (SDN) is a revolutionary technology that facilitates network management and enables programmatically efficient network configuration, thereby improving network performance and flexibility. However, as the application programming interfaces (APIs) of SDN are low-level or functionality-restricted, SDN programmers cannot easily keep pace with the ever-changing devices, topologies, and demands of SDN. By deriving motivation from industry practice, we define a novel network algorithm programming language (NAPL) that enhances the SDN framework with a rapid programming flow from topology-based network models to C+ implementations, thus bridging the gap between the limited capability of existing SDN APIs and the reality of practical network management. In contrast to several state-of-the-art languages, NAPL provides a range of critical high-level network programming features:linebreak (1) topology-based network modeling and visualization; (2) fast abstraction and expansion of network devices and constraints; (3) a declarative paradigm for the fast design of forwarding policies; (4) a built-in library for complex algorithm implementation; (5) full compatibility with C+ programming; and (6) user-friendly debugging support when compiling NAPL into highly readable C+ codes. The expressiveness and performance of NAPL are demonstrated in various industrial scenarios originating from practical network management.