Current Software-Defined Networking Research Articles

DFRDRL: a dynamic fuzzy routing algorithm based on deep reinforcement learning with guaranteed latency and bandwidth for software-defined networks

Traditional routing algorithms have several limitations that become increasingly significant in the context of Software-Defined Networking (SDN). Firstly, these algorithms often have limited support for Quality of Service (QoS) requirements, making them less suitable for handling diverse traffic types efficiently. Moreover, current SDN controllers leverage a default shortest-path routing approach, which does not allow for more dynamic and flexible traffic management based on real-time network policies. To address these issues, this paper introduces a Dynamic Fuzzy Routing algorithm based on Deep Reinforcement Learning (DRL) for SDN that provides guaranteed latency and bandwidth (DFRDRL). DFRDRL provides intelligent and efficient routing that adapts to dynamic traffic by considering path-state criteria and using DRL. Fuzzy logic mainly performs online routing between an ingress-egress pair. To adapt to dynamic traffic changes, DFRDRL can predict the traffic matrix in real time. Meanwhile, DFRDRL reduces the routing reliance on network topology by weighting the network based on critical nodes. Additionally, when the network experiences congestion based on the traffic matrix, a deferral mechanism is applied to prioritize requests with lower resource demands. This mechanism helps ensure efficient resource allocation by temporarily postponing or queuing higher-demand requests, thereby optimizing overall network performance during periods of congestion. The simulation results show that the performance of DFRDRL is better than the equivalent algorithms in terms of latency and throughput. Also, DFRDRL is about 2.5% more efficient than the best existing algorithm in terms of admission rate of routing requests.

A SDN improvement scheme for multi‐path QUIC transmission in satellite networks

AbstractIn recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS.

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.

The LOFT Attack: Overflowing SDN Flow Tables at a Low Rate

The emerging Software-Defined Networking (SDN) is being adopted by data centers and cloud service providers to enable flexible control. Meanwhile, the current SDN design brings new vulnerabilities. In this paper, we explore a stealthy attack that uses a minimum rate of attack packets to disrupt SDN data plane. To achieve this, we propose the LOFT attack that computes the lower bound of attack rate to overflow flow tables based on the inferred network configurations. Particularly, each attack packet always triggers or maintains consumption of one flow rule. LOFT can ensure the attack effect under various network configurations while reducing the possibility of being captured. We demonstrate its feasibility and effectiveness in a real SDN testbed consisting of commercial hardware switches. The experimental results show that LOFT incurs significant network performance degradation and potential network DoS at an attack rate of only tens of Kbps. To defeat the attack, we develop a data-to-control plane collaborative defense system named LOFTGuard, which is lightweight and transparent to SDN applications. Evaluations show that Guard effectively protects SDN against the attack and introduces a small overhead.

Flexible handover solution for vehicular ad-hoc networks based on software defined networking and fog computing

<p>Vehicular ad-hoc networks (VANET) suffer from dynamic network environment and topological instability that caused by high mobility feature and varying vehicles density. Emerging 5G mobile technologies offer new opportunities to design improved VANET architecture for future intelligent transportation system. However, current software defined networking (SDN) based handover schemes face poor handover performance in VANET environment with notable issues in connection establishment and ongoing communication sessions. These poor connectivity and inflexibility challenges appear at high vehicles speed and high data rate services. Therefore, this paper proposes a flexible handover solution for VANET networks by integrating SDN and fog computing (FC) technologies. The SDN provides global knowledge, programmability and intelligence functions for simplified and efficient network operation and management. FC, on the other hand, alleviates the core network pressure by providing real time computation and transmission functionalities at edge network to maintain the demands of delay sensitive applications. The proposed solution overcomes frequent handover challenges and reduces the processing overhead at core network. Moreover, the simulation evaluation shows significant handover performance improvement of the proposed solution compared to current SDN based schemes, especially in terms of handover latency and packet loss ratio under various simulation environments.</p>

Defense Against Software-Defined Network Topology Poisoning Attacks

Software-Defined Network (SDN) represents a new network paradigm. Unlike conventional networks, SDNs separate control planes and data planes. The function of a data plane is enabled using switches, whereas that of a control plane is facilitated by a controller. The controller learns network topologies and makes traffic forwarding decisions. However, some serious vulnerabilities are gradually exposed in the topology management services of current SDN controller designs. These vulnerabilities mainly exist in host tracking and link discovery services. Attackers can exploit these weak points to poison the network topology information in SDN controllers. In this study, a novel solution is proposed to defend against topology poisoning attacks. By analyzing the existing topology attack principles and threat models, this work constructs legal conditions for host migration to detect host hijacking attacks. The checking of the Link Layer Discovery Protocol (LLDP) source and integrity is designed to defend against link fabrication attacks. A relay-type link fabrication attack detection method based on entropy is also designed. Results show that the proposed solution can effectively detect existing topological attacks and provide complete and comprehensive topological security protection.

Performance evaluation of software-defined networking controllers in wired and wireless networks

Traditional networking solutions are unable to meet modern computing needs due to the expanding popularity of the internet, which requires increased agility and flexibility. To meet these objectives, software-defined networking (SDN) arises. A controller is a major element that will determine if SDN succeeds or fails. Various current SDN controllers in many sectors must be evaluated and compared. The performance of two well-known SDN controllers, POX and Ryu, is evaluated in this research. We used the Mininet-WiFi emulator to implement our work and the distributed internet traffic generator (D-ITG) to assess the aforementioned controllers using delay, jitter, packet loss, and throughput metrics. What is new in our research is the study of network performance in two different types of transmission media: wired and wireless. The speed of the wired medium was chosen to be fast ethernet, which was not previously studied. In addition, the size of the packet was varied among 128, 256, 512, and 1,024 bytes. The comparison was performed on three topologies (single, linear, and tree). The experimental results showed that Ryu offers significantly lower latency, jitter, and packet loss than POX in most scenarios. Also, the Ryu controller has higher throughput than POX, especially on wireless networks.

P4-TINS: P4-Driven Traffic Isolation for Network Slicing With Bandwidth Guarantee and Management

Network slicing is an essential technology for 5G mobile networks. It partitions network resource logically into multiple isolated slices, each of which can satisfy a suite of network requirements for one specific service. However, it cannot be fulfilled by the current SDN (Software-Defined Networks), since the conventional SDN data-plane technology, OpenFlow, is not flexible enough to offer fine-grained network resource control or queue/packet scheduling. It leads to many research studies developing corresponding solutions on programmable switches. In this work, we focus on the support of the bandwidth guarantee and management for network slices. Although several studies with the similar goal have been proposed, they do not consider interference among different flow types or use the built-in meter for easy deployment on COTS (Commercial Off-The-Shelf) P4 switches. To this end, we first conduct a case study to examine the interference cases. We then propose a solution, designated as P4-TINS (P4-driven Traffic Isolation for Network Slicing), to resolve the interference by isolating different types of traffic flows in priority queues and set the P4 switch’s bucket size based on the time granularity of its bandwidth management operation. It cannot only ensure the guaranteed bandwidth for each slice but also enable coexistent slices to fairly share residual bandwidth. We have confirmed its effectiveness experimentally based on our prototype over an ONOS (Open Network Operating System) controller and a COTS P4 switch.

Controller placement problem in software defined networks

<span>The requirements <span>for the network are increasing by the expanding and spreading the Internet. The Previous techniques of the network do not meet the modern needs, thus, a new technical presents software-defined networking (SDN). SDN recognizes as a promising new model that separates the control plane (traffic routing and network topology) from the data plane (network architecture layers). The architecture of SDN has some features that find in a single controller or many controllers instance of programmable, flexible, and scalable. In the current SDN, multiple controllers are essential. Therefore, the optimal number of the controllers and their locations is the most significant challenge, known as the controller placement problem (CPP). It deploys the optimal number of controllers within the network while meeting presentations requirements considered conflicting in nature example: credibility, load balancing, latency, energy efficiency, and computation time. Many studies researched the ways to develop solutions for improving scalability, place selection for SDN. This paper presents the CPP and gives a comprehensive review of SDN issues based on the recent well-known research to extract available solution strategies. Finally, it discusses the limitations and future study directions that can support researchers in this field.</span></span>

Dynamic Feedback versus Varna-Based Techniques for SDN Controller Placement Problems

During the past few years, software-defined networking (SDN) has become a successful architecture that decouples the control plane from the data plane. SDN has the capability to monitor and control the network in a central fashion through a softwarization process. The central element is the controller. For the current SDN architectures, there is an essential need for multiple controllers. The process of placing the controllers efficiently in an SDN environment is called the controller placement problem (CPP). Earlier CPP solutions focused on improving the propagation delays through the capacity of the controllers and the dynamic load on the switches. In this paper, we develop a novel algorithm called dynamic feedback algorithm for controller placement for SDN (DFBCPSDN). DFBCPSDN is compared with the varna-based optimization (VBO) towards solving the CPP. We used the VBO as the reference model to this work since it is relatively a new algorithm. Moreover, the VBO extensively outperformed many other existing models. To the best of our knowledge, this is one of the first attempts to minimize the total average latency of SDN using feedback control theoretic techniques. Experimental results indicate that the DFBCPSDN outperforms the VBO algorithm implemented in two well-known topologies, namely Internet2 OS3E topology and EU-GÉANT topology. We observe that for uncapacitated CPP, the DFBCPSDN outperforms the VBO for Internet2 OS3E and EU-GÉANT topologies by 11% and 9%, respectively, in terms of total average latency. On the other hand, for capacitated CPP, the DFBCPSDN algorithm outperforms the VBO reference model by 10% and 8%, respectively.

RLMR: Reinforcement Learning Based Multipath Routing for SDN

In recent years, as a new subject in the computer field, artificial intelligence has developed rapidly, especially in reinforcement learning (RL) and deep reinforcement learning. Combined with the characteristics of Software Defined Network (SDN) for centralized control and scheduling, resource scheduling based on artificial intelligence becomes possible. However, the current SDN routing algorithm has the problem of low link utilization and is unable to update and adjust according to the real-time network status. This paper aims to address these problems by proposing a reinforcement learning-based multipath routing for SDN (RLMR) scheme. RLMR uses Markov Decision Process (MDP) and Q-Learning for training. Based on the real-time information of network state and flow characteristics, RLMR performs routing for different flows. When there is no link that meets the bandwidth requirements, the remaining flows are redistributed according to the Quality of Service (QoS) priority to complete the multipath routing. In addition, this paper defines the forward efficiency (FE) to measure the link bandwidth utilization (LBU) under multipath routing. Simulation results show that compared with the current mainstream shortest path algorithm and ECMP algorithm, the routing algorithm in RLMR has advantages in FE, jitter, and packet loss rate. It can effectively improve the efficiency and quality of routing.

Optimisation Methods for the Controller Placement Problem in SDN: A Survey

Software-defined networking (SDN) has emerged in response to increasing requirements for new networks and expansion of Internet coverage. Modern needs exceed the limitations of traditional networks, for which, to simplify management, SDN is proposed as a promising paradigm that separates the control and data planes, allowing for the programming of network configuration. SDN deployment and applications are directly affected by the controller position. Single or multiple controllers are used in SDN architecture to enable programmable, flexible, and scalable configurations. Multiple controllers are essential in the current SDN, and various solutions have been recently developed to improve scalability and placement selection. In this study, the Controller Placement Problem (CPP) is explored using objective optimisation with proposed algorithms. An overview of SDN issues and the controller role is provided through its three-plane architecture with a focus on scalability and reliability. In addition, a comprehensive problem review is discussed on the basis of a well-known compendium of available solutions. Finally, relevant open problems and future research challenges are identified.

Software-Defined Networking Meets Software-Defined Radio in Mobile ad hoc Networks: State of the Art and Future Directions

The aims of this survey article are to elaborate on cross-layer optimization, Software-Defined Networking (SDN) and Software-Defined Radio (SDR) as separate domains of wireless network design for which a unified view has not been adequately considered to date and present lessons learned, with a view towards the challenges associated with SDN-SDR interaction that would facilitate benefits in cross-layer optimization of mobile ad hoc networks (MANETs). We focus on MANETs because ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula> ) they are still at the forefront of technology, and in some scenarios they are the only meaningful option for establishing communication; ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) they expose the full potential and benefits of coexistence and interaction of SDN and SDR, in terms of optimizing key performance indicators. While SDN and SDR are mature technologies, their interaction and joint consideration have been largely overlooked. Current SDN approaches do not span the physical (PHY) and medium-access control (MAC) layers, but they rather concentrate on network-level routing and traffic flow optimization. As a result, PHY- and MAC-layer related parameters which notoriously affect key network performance metrics remain static or at best are adapted based on some heuristic or local approaches. On the other hand, the reach of SDR architectures is restricted to the PHY and MAC layers. We discuss the state of the art of cross-layer optimization, SDN and SDR, and current challenges associated with coexistence and interaction of SDN and SDR. Such an interaction would extend the span of SDN to PHY and MAC layers and lead to realizations of centralized approaches across all layers so as to control and optimize parameters, towards global network objectives. It would also create a bridge between centralized network control that is inherent in SDN and the distributed nature of MANETs, with the add-on features of flexible and fast PHY and MAC layer adaptation offered by SDR, for solid, autonomous and ultimately better network control implementations that span all layers, towards realizing and implementing the holy grail of real cross-layer optimization.

Enhancing 5G SDN/NFV Edge with P4 Data Plane Programmability

The 5G network revolution will be enabled by deep integration of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to support multi-tenancy, per-user and per-application quality of service and experience. However, full softwarization and current SDN platforms may not be able to sustain the complexity and the heterogeneity of different requirements, for example, strict latency, jitter, high precision traffic and advanced monitoring. For such services, SDN/NFV needs to be boosted not only considering orchestration and control plane, but also data plane programmability. In this article, the potential of the P4 language is illustrated with the aim to show its disruptive novel functionalities at the data plane level currently not available in a SDN/NFV network, opening the way to new orchestration frameworks and enabling a novel autonomic and flexible network at the edge. Use cases, assessments and softwarized performance results are proposed and discussed in the edge and IoT scenario, targeting advanced traffic engineering, cyber security, multi-tenancy, 5G offloading, and telemetry, to demonstrate the feasibility of such an approach.

Achieving Balanced Load Distribution with Reinforcement Learning-Based Switch Migration in Distributed SDN Controllers

Distributed controllers in software-defined networking (SDN) become a promising approach because of their scalable and reliable deployments in current SDN environments. Since the network traffic varies with time and space, a static mapping between switches and controllers causes uneven load distribution among controllers. Dynamic migration of switches methods can provide a balanced load distribution between SDN controllers. Recently, existing reinforcement learning (RL) methods for dynamic switch migration such as MARVEL are modeling the load balancing of each controller as linear optimization. Even if it is widely used for network flow modeling, this type of linear optimization is not well fitted to the real-world workload of SDN controllers because correlations between resource types are unexpectedly and continuously changed. Consequently, using the linear model for resource utilization makes it difficult to distinguish which resource types are currently overloaded. In addition, this yields a high time cost. In this paper, we propose a reinforcement learning-based switch and controller selection scheme for switch migration, switch-aware reinforcement learning load balancing (SAR-LB). SAR-LB uses the utilization ratio of various resource types in both controllers and switches as the inputs of the neural network. It also considers switches as RL agents to reduce the action space of learning, while it considers all cases of migrations. Our experimental results show that SAR-LB achieved better (close to the even) load distribution among SDN controllers because of the accurate decision-making of switch migration. The proposed scheme achieves better normalized standard deviation among distributed SDN controllers than existing schemes by up to 34%.

Taxonomy of controller placement problem (CPP) optimization in Software Defined Network (SDN): a survey

The new network requirements are rising with internet scalability and expansion of its coverage. The traditional network cannot support recent needings, so a new architecture has been proposed for the future networks called Software Defined Network (SDN). This architecture makes the network more programmable, flexible, and controllable, though the scalability is the most important challenge on which the researchers are working in SDN. Multiple controllers are a necessity of current SDN, so the optimum number of controllers and their placement is a problem called controller placement problem (CPP). There are defined different functions to optimize scalability, reliability, and others in SDN which consider various metrics in recent research papers. This optimization problem is NP-hard. In this paper, we survey the CPP optimization in recent well-known papers to extract optimization strategies, objective functions, and solutions. We finally reveal a new taxonomy of cutting-edge studies about solutions of CPP optimization in the SDN from different dimensions. CPP solutions based on objective functions, metrics and traits are classified in this article. Furthermore, we put forward the new challenges and metrics which can be reckoned in CPP to be optimized. This paper can be useful for those working on CPP optimization in SDN to expose the objective functions, variables and metrics.

A survey on resolving security issues in SaaS through software defined networks

The key ingredient in the success of Software-as-a-Service (SaaS) is based upon the client's satisfaction. Sensitive data acquired from the organisations are processed by the SaaS applications and stored at the SaaS provider end. All data flow over the network needs to be secured to avoid leakage of sensitive data. However, upon preliminary investigation, security is found to be the foremost issue that hampers the growth of confidence in the entrepreneurs for data deployment. This paper mainly focuses on different security issues in SaaS. Further, we analyse the security issues derived from the use of Software Defined Networking (SDN) and elaborate on how it helps to improve security in SaaS. Additionally, comparisons between current solutions and SDN solutions are made. Hence, this work aims at giving new directions to the researchers, specifically in the domain of SaaS, in understanding security issues and planning possible countermeasures.

High-performance flow classification using hybrid clusters in software defined mobile edge computing

Mobile Edge Computing (MEC) provides different storage and computing capabilities within the access range of mobile devices. This moderates the burden of offloading compute/storage-intensive processes of the mobile devices to the centralized cloud data centers. As a result, the network latency is reduced and the quality of service provided for the mobile end users is improved. Different applications benefit from the large-scale deployments of MEC servers. However, the considerable complexity of managing large scale deployments of the sheer number of applications for the millions of mobile devices is a challenge. Recently, Software Defined Networking (SDN) is leveraged to resolve the problem by providing unified and programmable interfaces for managing network devices. Most of the current SDN packet processing services are tightly dependent on the packet classification service. This primary service classifies any incoming packet based on matching a set of specific fields of its header against a flow table. Acceleration of this basic process considerably increases the performance of the SDN-based MEC. In this paper, the hierarchical tree algorithm, which is a packet classification method, is parallelized using popular platforms on a cluster of Graphics Processing Units (GPUs), a cluster of Central Processing Units (CPUs), and a hybrid cluster. The best scenario for the parallel implementation of this algorithm on the CPU cluster is that which combines OpenMP and MPI.In this case, the throughput of the classifier is 4.2 million packets per second (MPPS). On the GPU cluster, two different scenarios have been used. In the first scenario, the global memory is used to store the rules and the Hierarchical-trie of the classifier while in the second scenario we break the filter set in a way that the resulting Hierarchical-trie of each subset could be stored in the shared memory of GPU. According to the results, although the first GPU cluster scenario achieves a throughput of 29.19 MPPS and a speedup 58 times as great as the serial mode, the second scenario is 12 times faster due to using the shared memory. The best performance, however, belongs to the hybrid cluster mode. The hybrid cluster achieves a throughput of 30.59 which is 1.4 MPPS more than the GPU cluster.

An SDN Controller Security Cluster Scheme Based on Intrusion Detection Technology

Software Defined Network (SDN) is a new network innovation architecture that separates the control layer and the data layer of the network. In the SDNs, the control layer is the core of the entire network, and its security is very important. In practical applications, the control layer is usually composed of SDN controller clusters. In order to enhance the security of the SDN controller cluster, this paper first studies the development status of the current SDN technology, and then analyzes the current state of the defense technology of the network system. Through the analysis and summary of the two, we proposed the discovery and isolation scheme of the invaded controller in the cluster environment, and designed the intrusion detection module, control module and defense module. Finally, we implemented the scheme based on Opendaylight controller, and proved the effectiveness of the scheme by experiments.

Adaptive Telemetry for Software-Defined Mobile Networks

The forthcoming set of 5G standards will bring programmability and flexibility to levels never seen before. This has required introducing changes in the architecture of mobile networks, enabling different features such as the split of control and data planes, as required to support the rapid programming of heterogeneous data planes. Software Defined Networking (SDN) has emerged as a basic toolset for operators to manage their infrastructure, as it opens up the possibility of running a multitude of intelligent and advanced applications for network optimization purposes in a centralized network controller. However, the very basic nature that makes possible this efficient management and operation in a flexible way—the logical centralization—poses important challenges due to the lack of proper monitoring tools, suited for SDN-based architectures. In order to take timely and right decisions while operating a network, centralized intelligence applications need to be fed with a continuous stream of up-to-date network statistics. However, this is not feasible with current SDN solutions due to scalability and accuracy issues. This article first analyzes the monitoring issues in current SDN solutions and then proposes a telemetry framework for software defined mobile networks capable of adapting to the various 5G services. Finally, it presents an experimental validation that shows the benefits of the proposed solution at alleviating the load on the control and data planes, improving the reactiveness to network events, and providing better accuracy for network measurements.