Software Defined Networking Architecture Research Articles

An Integrated DQN and RF Packet Routing Framework for the V2X Network

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.

A Lightweight Packet Forwarding Verification in SDN using Sketch

By decoupling the control plane and the data plane, Software Defined Networking (SDN) has reshaped the ossified network architecture and improved the programmability of the network. However, SDN is also susceptible to malicious injection, tampering, dropping and hijacking attacks against forwarding packets, and the SDN architecture cannot perceive the real behavior of switches in the data plane. Packet forwarding verification and exception localization are recognized as effective and promising methods, enabling reliable packet delivery in the data plane and allowing the controller in SDN to identify abnormal links. While existing mechanisms embed the linear-scale cryptographic tags into the packet header space as the transmission path lengthens to realize packet verification and exception localization, which cannot achieve a feasible tradeoff between efficiency and security. Leveraging the central controllability of SDN, we propose a lightweight packet forwarding verification mechanism. This mechanism splits the runtime of a flow into consecutive epochs by address hopping. The switches in the data plane forward packets based on hopping address, collecting the information of packets forwarded in a compact data structure, namely traffic sketch. The controller verifies traffic sketches and localizes exception in the epoch. We further prototype the proposed mechanism based on simulation network. The analysis and experiments demonstrate that the cost of proposed scheme is lower than the similar mechanisms, introducing no more than 9% additional forwarding delay and less than 8% throughput degradation.

An effective deep-Q learning scheme for QoS improvement in physical layer of software-defined networks

The demands of diverse applications necessitate specific communication requirements, such as jitter, packet loss ratio (PLR), and latency, at the end-to-end (E2E) level within the physical layer of software-defined networking (SDN). In heterogeneous network environments, E2E routes traverse multiple domains with varying quality-of-service (QoS) classes of traffic, posing challenges in meeting unique E2E QoS criteria. Hence the performance on the E2E path is significant to meet the needs of applications. Hence, the issue of providing services on the E2E route is challenging due to several QoS classes in a domain on the source-to-destination route. This paper presents an SDN architecture leveraging Deep Q-learning (DQL) to ascertain the appropriate QoS class for E2E routes in software-defined heterogeneous networks, thereby reducing overall E2E delay. The proposed model with DQL is compared with benchmark reinforcement learning. The proposed framework is validated using various real Internet topologies such as Abilene, USNet, and OS3E, assessing not only E2E delay but also jitter, PLR, and throughput, showcasing its efficacy in optimizing network performance regarding these performance metrics.

Utilizing Extremely Fast Decision Tree (EFDT) Algorithm to Categorize Conflict Flow on a Software-Defined Network (SDN) Controller

Software-Defined Networks (SDNs) provide a contemporary approach to networking technology, offering a versatile and dynamically efficient network architecture for enhanced surveillance and performance. However, SDN architectures may encounter flow conflicts. These conflicts arise when modifications are made to specific flow properties, such as priority, match field, and action. Despite the existence of recommended solutions, the process of resolving conflicts in SDN continues to encounter difficulties. This study proposes an Extremely Fast Decision Tree (EFDT) classification technique to detect and categorize conflicts inside the flow table. The novelty of this method is based on the development of an accurate and effective machine-learning technique implemented on the Ryu controller plane and validated using the Mininet simulator. The effectiveness and efficiency of the proposed method were evaluated using various indicators, demonstrating superior performance in recognizing and categorizing conflict flow types in all flow sizes ranging from 10,000 to 100,000.

Blockchain-Enabled Intelligent IoT Protocol for High-Performance and Secured Big Financial Data Transaction

In the recent era, the communication network with the support of many wireless technologies is giving benefits for remote access. Such a communication model increases the flexibility for data storage with the management of network resources efficiently with the integration of the Internet of Things (IoT). Although, the industrial Internet of Things (IIoT) has enabled the development of numerous machine learning-based solutions to provide real-time applications. However, most of the solutions are not prepared to cope with heterogeneous services and the huge amount of device data in the digital world. Furthermore, conducting financial transactions over the Internet raises several security issues. Such restrictions compromise the sensitive data of financial institutions and also degrade the trust of network users in the system. Thus, this article presents a secured blockchain model for high-performance computing in a big data environment, which aims to protect the business activities for financial interaction with intelligent services of software-defined network (SDN) architecture. First, to keep the security credentials, the SDN controller creates an association between the IoT devices and maintains local and global records. Second, the machine learning approach is explored using reliable and fault-tolerant methods to support the network scalability and extract the updated routing information for transmitting financial data. The proposed protocol also provides data integrity with a high level of network availability and copes with the financial security of big data by investigating cryptographic approaches. Our proposed protocol is tested using simulation, and various experiments are performed to show its efficacy in terms of network throughput, computing overhead, data delay, response time, and dropped packets as compared to tunicate swarm algorithm-based optimized routing mechanism (TORM) and RouteChain.

An Adaptive State Consistency Architecture for Distributed Software-Defined Network Controllers: An Evaluation and Design Consideration

The Physically Distributed Logically Centralized (PDLC) software-defined network (SDN) control plane is physically dispersed across several controllers with a global network view for performance, scalability, and fault tolerance. This design, providing control applications with a global network view, necessitates network state synchronization among controllers. The amount of inter-controller synchronization can affect the performance and scalability of the system. The absence of standardized communication protocols for East-bound SDN interfaces underscores the need for high-performance communication among diverse SDN controllers to maintain consistent state exchange. An inconsistent controller’s network view can significantly impact network effectiveness and application performance, particularly in dynamic networks. This survey paper offers an overview of noteworthy AI and non-AI solutions for PDLC SDN architecture in industry and academia, specifically focusing on their approaches to consistency and synchronization challenges. The suggested PDLC framework achieves an adaptive controller-to-controller synchronization rate in a dynamic network environment.

Enhanced Anomaly Detection Framework for 6G Software-Defined Networks: Integration of Machine Learning, Deep Neural Networks, and Dynamic Telemetry

The advent of 6G networks ushers in a new era of intelligent network management, necessitating robust security measures to safeguard against emerging threats. This paper presents a comprehensive framework for anomaly detection tailored specifically for 6G Software-Defined Networks (SDNs), leveraging innovative ML), (DL), and dynamic telemetry techniques. The proposed framework, termed Anomaly Detection System for 6G SDNs, integrates ensemble learning (EL) algorithms and deep neural networks (DNNs) to detect anomalies within network traffic. Beginning with the preprocessing and feature selection stages, the proposed system employs an amalgam EL method to enhance the efficacy of anomaly detection. Datasets including CICDDOS2019, NSL KDD, CIC_IDS2017, and NB2015 undergo dimensionality reduction and feature subset determination to optimize performance. Furthermore, dynamic telemetry is seamlessly integrated into the proposed, enabling real- time monitoring and adaptive response mechanisms within SDN environments. By harnessing the flexibility and programmability of SDNs, the framework ensures a proactive defense against evolving threats, bolstering the security posture of 6G networks. Experimental evaluations demonstrate the effectiveness of ADS6SDN across diverse datasets, achieving high accuracies while minimizing false alarm rates. In conclusion, integrating ML, DL, and dynamic telemetry within the proposed approach offers a potent solution for enhancing the security and responsiveness of 6G SDNs. By leveraging the inherent advantages of SDN architectures, the framework not only fortifies network defenses against emerging threats but also ensures adaptability to the budding scenario of next-generation telecommunications.

Comparative Analysis of Cloud based SDN and NFV in 5g Networks

The acceleration of 5G and other communication technologies is significantly influenced by digitization. However, the current state of 5G development falls short in meeting various performance and manageability requirements, particularly in supporting data center networks. To overcome these challenges, telecom enterprises commonly utilize Network Function Virtualization (NFV) and Software-Defined Networking (SDN) in their data centers. This paper introduces a comprehensive SDN-NFV architecture aimed at simplifying network administration tasks for telecom enterprises. The evolution of interconnect network operations for virtual services has undergone substantial transformations due to the adoption of Software-Defined Networking (SDN) and Network Function Virtualization (NFV). We conducted an investigation into the SDN/NFV paradigm to determine the effective implementation, management, and distribution of network services to end users. This compilation of research papers delves into various aspects of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) within the realm of emerging technologies. Aissioui et al. (2015) contribute with an elastic distributed SDN/NFV controller designed for the efficient management of 5G mobile cloud systems, addressing their dynamic nature. Alenezi et al. (2019) focus on cloud-based SDN and NFV architectures tailored for IoT infrastructure, emphasizing scalability and flexibility. Barakabitze et al. (2020) provide an extensive survey on 5G network slicing using SDN and NFV, categorizing approaches and discussing future challenges.

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.

A cost and demand sensitive adjustment algorithm for service function chain in data center network

The introduction of Network Function Virtualization (NFV) and Software-Defined Network (SDN) architectures has significantly reduced the Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) of network system. However, NFV orchestration management also brings about challenges. After the initial deployment of VNFs, due to the volatility of network requests, the original deployment may not be able to meet user resource demands. The key issue is how to readjust resources dynamically to accommodate more network requests without violating Quality of Service (QoS) for users. Several existing techniques can be used to achieve this goal, such as horizontal scaling, vertical scaling, and virtual network function (VNF) migration. However, these techniques inevitably incur some overhead, such as the cost of instantiating VNF and link rerouting. Additionally, resource adjustment may also result in unbalanced distribution of network resources. In this paper, an Intelligent Service Function Chain Dynamic Adjustment Algorithm (ISFCDAA) is proposed to address the above challenges. Firstly, an Integer Linear Programming (ILP) model is established with the objective of minimizing the long-term adjustment cost and reducing the imbalance of resource distribution. Then we transform the optimization process into a Markov Decision Process (MDP). Secondly, to solve the problems that the state and action space is too large and the state transition probability is uncertain in MDP, a SFC dynamic adjustment algorithm based on deep reinforcement learning is proposed. This algorithm can obtain an approximate optimal adjustment strategy for ILP model. The simulation results show that ISFCDAA can reduce the adjustment overhead and maintain a balanced distribution of network resources while ensuring the QoS. Compared with the existing algorithms, the average standard deviation of resource distribution of ISFCDAA is reduced by up to 9.90%, the average acceptance rate of ISFCDAA is improved by up to 39.57%, and the average long-term profit is improved by up to 42.92%. The incorporation of cost and demand-sensitive considerations into ISFCDAA enhances its responsiveness to fluctuating network demands, solidifying its effectiveness in dynamic resource management scenarios.

A Temporal Deep Q Learning for Optimal Load Balancing in Software-Defined Networks.

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward-punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach-tDQN-effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss.

Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems.

Bi-channel hybrid GAN attention based anomaly detection system for multi-domain SDN environment

Software-Defined Networking (SDN) is a strategy that leads the network via software by separating its control plane from the underlying forwarding plane. In support of a global digital network, multi-domain SDN architecture emerges as a viable solution. However, the complex and ever-evolving nature of network threats in a multi-domain environment presents a significant security challenge for controllers in detecting abnormalities. Moreover, multi-domain anomaly detection poses a daunting problem due to the need to process vast amounts of data from diverse domains. Deep learning models have gained popularity for extracting high-level feature representations from massive datasets. In this work, a novel deep neural network architecture, supervised learning based LD-BiHGA (Low Dimensional Bi-channel Hybrid GAN Attention) system is designed to learn class-specific features for accurate anomaly detection. Two asymmetric GANs are employed for learning the normal and abnormal network flows separately. Then, to extract more relevant features, a bi-channel attention mechanism is added. This is the first study to introduce an innovative hybrid architecture that merges bi-channel hybrid GANs with attention models for the purpose of anomaly detection in a multi-domain SDN environment that effectively handles real-time unbalanced data. The suggested architecture demonstrates its effectiveness on three benchmark datasets, achieving an average accuracy improvement of 7.225% on balanced datasets and 3.335% on imbalanced datasets compared to previous intrusion detection system (IDS) architectures in the literature.

A novel dual optimized IDS to detect DDoS attack in SDN using hyper tuned RFE and deep grid network

Technological advancement is one of the factors contributing to a rise of susceptible cyberattacks. Distributed denial of service (DDoS) attack reduces the efficiency of network servers by saturating them with unwanted data and preventing authorized clients from accessing them. Due to the centralized architecture of Software Defined Network (SDN), it faces a number of security vulnerabilities. In SDN, DDoS attack is one of the main strikes on the control planes. A novel Optimized Dual Intrusion Detection System is proposed to identify DDoS and Non-DDoS attack more quickly with best proposed models. Hyper Tuned parameter optimization is carried on Logistic Regression, Decision Tree and Random Forest algorithms to find the best parameters. RFE with Repeated Stratified K-fold feature selection is used using the best parameters to reduce the 77 features to 4 features. A novel Deep Grid Network combines hyper-tuned classifiers with 7 other machine learning algorithms to produce 21 models. An ensemble technique uses 6 best models from 21 models for the best prediction of DDoS attack. A new dataset is also generated through Mininet for proper validation of the model.

Freshness-Aware Age Optimization for Multipath TCP Over Software Defined Networks

Recently increasing real-time network applications have raised a new requirement for the future 6G network transmission, which desires the information-update packets arrive at the receiver as timely as possible. Multipath TCP (MPTCP) can be employed in the real-time update transmission system by making use of multiple paths so as to promote the information status update. However, current optimization solutions of MPTCP focus on traditional transmission performance such as throughput, delay, loss, jitter, etc. None of these works considers the freshness metric for MPTCP to satisfy the real-time demands. What's more, most works about transmission model in Software Defined Network (SDN) are based on packet-level analysis without considering flow-level situation. In order to fill these gaps, this paper proposes a novel freshness-aware age optimization solution for MPTCP (FAMT) over SDN. FAMT introduces a new four-tuples to describe the transmission state in the SDN architecture. Then FAMT classifies the whole transmission process into two phases: connection establish phase and data transmission phase. Based on the two phases, FAMT evaluates and defines the age of information for different situations. In addition, FAMT designs an innovative age-assisted fluid model of MPTCP which can realize the four goals effectively.

Secure multi-path routing for Internet of Things based on trust evaluation.

In the realm of the Internet of Things (IoT), ensuring the security of communication links and evaluating the safety of nodes within these links remains a significant challenge. The continuous threat of anomalous links, harboring malicious switch nodes, poses risks to data transmission between edge nodes and between edge nodes and cloud data centers. To address this critical issue, we propose a novel trust evaluation based secure multi-path routing (TESM) approach for IoT. Leveraging the software-defined networking (SDN) architecture in the data transmission process between edge nodes, TESM incorporates a controller comprising a security verification module, a multi-path routing module, and an anomaly handling module. The security verification module ensures the ongoing security validation of data packets, deriving trust scores for nodes. Subsequently, the multi-path routing module employs multi-objective reinforcement learning to dynamically generate secure multiple paths based on node trust scores. The anomaly handling module is tasked with handling malicious switch nodes and anomalous paths. Our proposed solution is validated through simulation using the Ryu controller and P4 switches in an SDN environment constructed with Mininet. The results affirm that TESM excels in achieving secure data forwarding, malicious node localization, and the secure selection and updating of transmission paths. Notably, TESM introduces a minimal 12.4% additional forwarding delay and a 5.46% throughput loss compared to traditional networks, establishing itself as a lightweight yet robust IoT security defense solution.

MFFLR-DDoS: An encrypted LR-DDoS attack detection method based on multi-granularity feature fusions in SDN.

Low rate distributed denial of service attack (LR-DDoS) is a special type of distributed denial of service (DDoS) attack, which uses the vulnerability of HTTP protocol to send HTTP requests to applications or servers at a slow speed, resulting in long-term occupation of server threads and affecting the normal access of legitimate users. Since LR-DDoS attacks do not need to send flooding or a large number of HTTP requests, it is difficult for traditional intrusion detection methods to detect such attacks, especially when HTTP traffic is encrypted. To overcome the above problems, we proposed an encrypted LR-DDoS attack detection and mitigation method based on the multi-granularity feature fusion (MFFLR-DDoS) for software defined networking (SDN). This method analyzes the encrypted session flow from the time sequence of packets and the spatiality of session flow and uses different deep learning methods to extract features, to obtain more effective features for abnormal traffic detection. In addition, we used the advantages of SDN architecture to perform real-time defense against LR-DDoS attacks by the way of SDN controller issuing flow rules. The experimental results showed that the MFFLR-DDoS model had a higher detection rate than advanced methods, and could mitigate LR-DDoS attack traffic online and in real-time.

Моделирование алгоритма аутентификации трафика на стороне контроллера в программно-конфигурируемой сети

To ensure efficient and optimal transmission of heterogeneous user traffic in a network, it is necessary to take into account such conditions as frame delay, speed, frequency range, and queue overflow in the Ethernet switch. In software-defined networks (SDN), these conditions can be controlled, which distinguishes SDN from a conventional computer network. SDN is a new paradigm in the field of network telecommunications. Petri nets in SDN modeling can be used to create formal models, which can be checked for correctness and efficiency before being implemented in a real network. Petri nets are also helpful in representing the architecture of SDN in the form of a graph, which improves the understanding and visualization of the SDN work. In this paper, we present a solution to the problem of SDN vulnerability using CPN Tools. We develop an algorithm for traffic authentication on the controller side in SDN to ensure the security of traffic transmission and eliminate man-in-the-middle attacks in SDN. The research aim consists in simulating the proposed algorithm and its verification based on the apparatus of Petri nets. All the components of the model on Petri nets are described. A comparative analysis of algorithms for encryption speed is carried out, and the maximum speed of traffic encryption by the proposed algorithm is revealed.

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.

QuicSDN: Transitioning from TCP to QUIC for southbound communication in software-defined networks

In Software-Defined Networks (SDNs), the control plane and data plane communicate for various purposes such as applying configurations and collecting statistical data. While various methods have been proposed to reduce the overhead and enhance the scalability of SDNs, the impact of the transport layer protocol used for southbound communication has not been investigated. Existing SDNs rely on Transmission Control Protocol (TCP) to enforce reliability. In this paper, we show that the use of TCP imposes a considerable overhead on southbound communication, identify the causes of this overhead, and demonstrate how replacing TCP with Quick UDP Internet Connection (QUIC) protocol can enhance the performance of this communication. We introduce the quicSDN architecture to enable southbound communication in SDNs via the QUIC protocol. We present a reference architecture based on the standard, most widely-used protocols by the SDN community and show how the controller and switch are revamped to facilitate this transition. We compare, both analytically and empirically, the performance of quicSDN versus the traditional SDN architecture and confirm the superior performance of quicSDN. Our empirical evaluations in different settings demonstrate that quicSDN lowers communication overhead and message delivery delay by up to 82% and 45%, respectively, compared to SDNs using TCP for their southbound communication.