Software Defined Networking Control Plane Research Articles

MEC-Based Dynamic Controller Placement in SD-IoV: A Deep Reinforcement Learning Approach

The flow fluctuations in the highly dynamic Internet of Vehicles (IoV) make the IoV difficult to provide reliable and scalable wireless network services for the emerging applications in the 5 G and beyond era. The software-defined networks (SDN) could feasibly manage and optimize the network according to the network load. Controller placement is a critical problem in SDN to achieve its robustness and flexibility with the changes of network status. Motivated by the advantages of SDN and Mobile-edge computing (MEC), this paper aims at enhancing the performance of IoV with the assistance of these two. Specifically, we consider a three-layer hierarchical SDN control plane for the IoV, where the SDN controllers are placed at the edge of networks. Under this framework, we investigate a multi-objective optimization problem on controller placement problem including delay, load balancing, and path reliability. To efficiently solve the formulated NP-hard problems, we develop an algorithm based on multi-agent deep Q-learning networks (MADQN) because of its advantages for large-scale combinatorial optimization. At last, we use multi-process technology to accelerate the operation of the algorithm, so as to complete the algorithm iteration faster. Numerical results show that the proposed methods achieve better performances than three baselines.

QoS-aware resource allocation and fault tolerant operation in hybrid SDN using stochastic network calculus

Mobile edge computing (MEC) is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the users. With the advent of the next-generation mobile networks, researchers have turned their attention to various aspects of edge computing in an effort to leverage the new capabilities offered by 5G. So, the integration of software defined networking (SDN) and MEC techniques was seriously considered to facilitate the orchestration and management of Mobile Edge Hosts (MEH). Edge clouds can be installed as an interface between the local servers and the core to provide the required services based on the known concept of the SDN networks. Nonetheless, the problem of reliability and fault tolerance will be of great importance in such networks. The paper introduced a dynamic architecture that focuses on the end-to-end mobility support required to maintain service continuity and quality of service. This paper also presents an SDN control plane with stochastic network calculus (SNC) framework to control MEC data flows. In accordance with the entrance processes of different QoS-class data flows, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Compared to other solutions investigated in the literature, the proposed approach exhibits a significant increase in the throughput distributed over the active links of mobile edge hosts. It also proved that the outage index and the system’s aggregate data rate can be effectively improved by up to 32%.

Renaissance: A self-stabilizing distributed SDN control plane using in-band communications

By introducing programmability, automated verification, and innovative debugging tools, Software-Defined Networks (SDNs) are poised to meet the increasingly stringent dependability requirements of today's communication networks. However, the design of fault-tolerant SDNs remains an open challenge. This paper considers the design of dependable SDNs through the lenses of self-stabilization—a very strong notion of fault-tolerance. In particular, we develop algorithms for an in-band and distributed control plane for SDNs, called Renaissance, which tolerate a wide range of failures. Our self-stabilizing algorithms ensure that after the occurrence of arbitrary failures, (i) every non-faulty SDN controller can reach any switch (or another controller) within a bounded communication delay (in the presence of a bounded number of failures) and (ii) every switch is managed by a controller. We evaluate Renaissance through a rigorous worst-case analysis as well as a prototype implementation (based on OVS and Floodlight, and Mininet).

SDNShield: NFV-Based Defense Framework Against DDoS Attacks on SDN Control Plane

Software-defined networking (SDN) is increasingly popular in today’s information technology industry, but existing SDN control plane is insufficiently scalable to support on-demand, high-frequency flow requests. Weaknesses along SDN control paths can be exploited by malicious third parties to launch distributed denial-of-service (DDoS) attacks against the SDN control plane. Recently proposed solutions only partially solve the problem, by protecting either the SDN network edges or the centralized controller. We propose SDNShield, a solution based on emerging network function virtualization (NFV) technologies, which enforces more comprehensive defense against potential DDoS attacks on SDN control plane. SDNShield incorporates a three-stage overload control scheme. The first stage statistically identifies legitimate flows with low complexity and performance overhead. The second stage further performs in-depth TCP handshake verification to ensure good flows are eventually served. The third stage intellectually salvages the misclassified legitimate flows that are falsely dropped from the first two stages. Prototype tests and real data-driven simulation results show that SDNShield can achieve high resilience against brute-force attacks, and maintain good flow-level service quality at the same time.

Applying Federated Learning in Software-Defined Networks: A Survey

Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.

Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

Software-defined network (SDN) is an enabling technology that meets the demand of dynamic, adaptable, and manageable networking architecture for the future. In contrast to the traditional networks that are based on a distributed control plane, the control plane of SDN is based on a centralized architecture. As a result, SDNs are susceptible to critical cyber attacks that exploit the single point of failure. A distributed denial of service (DDoS) attack is one of the most crucial and risky attacks, targeting the SDN controller and disrupting its services. Several researchers have proposed signature-based DDoS mitigation and detection techniques that rely on manually configuring the policies. As the massive traffic from heterogeneous networks increases, conventional solutions are ineffective due to the lack of automation and human interference. This necessitates producing a detection solution, more effective than traditional ones, to ensure SDN security, resiliency, and availability. This paper addresses this problem by proposing a deep learning (DL)-based ensemble solution for efficient DDoS attack detection in SDN. Four hybrid models are presented by adopting three ensemble techniques and different DL architectures, namely convolutional neural network, long short-term memory, and gated recurrent unit, to improve the SDN traffic classification. The experimentation was conducted on the benchmark flow-based dataset CICIDS2017. High detection accuracy (99.77%) with a small number of flow-based features was achieved by our ensemble model, as our experimental results will demonstrate. The proposed solution was evaluated by several standard assessment matrices and by comparing against other state-of-the-art algorithms from the network security literature.

Software Defined Network Enabled Fog-to-Things Hybrid Deep Learning Driven Cyber Threat Detection System

Software Defined Network (SDN) is a next-generation networking architecture and its power lies in centralized control intelligence. The control plane of SDN can be extended to many underlying networks such as fog to Internet of Things (IoT). The fog-to-IoT is currently a promising architecture to manage a real-time large amount of data. However, most of the fog-to-IoT devices are resource-constrained and devices are widespread that can be potentially targeted with cyber-attacks. The evolving cyber-attacks are still an arresting challenge in the fog-to-IoT environment such as Denial of Service (DoS), Distributed Denial of Service (DDoS), Infiltration, malware, and botnets attacks. They can target varied fog-to-IoT agents and the whole network of organizations. The authors propose a deep learning (DL) driven SDN-enabled architecture for sophisticated cyber-attacks detection in fog-to-IoT environment to identify new attacks targeting IoT devices as well as other threats. The extensive simulations have been carried out using various DL algorithms and current state-of-the-art Coburg Intrusion Detection Data Set (CIDDS-001) flow-based dataset. For better analysis five DL models are compared including constructed hybrid DL models to distinguish the DL model with the best performance. The results show that proposed Long Short-Term Memory (LSTM) hybrid model outperforms other DL models in terms of detection accuracy and response time. To show unbiased results 10-fold cross-validation is performed. The proposed framework is so effective that it can detect several types of cyber-attacks with 99.92% accuracy rate in multiclass classification.

Power and Performance Efficient SDN-Enabled Fog Architecture

Software defined networks (SDNs) have dramatically simplified network management. However, enabling pure SDNs to respond in real-time while handling massive amounts of data still remains a challenging task. In contrast, fog computing has strong potential to serve large surges of data in real time. SDN control plane enables innovation, and greatly simplifies network operations and management thereby providing a promising solution to implement energy and performance aware SDN-enabled fog computing. Besides, power efficiency and performance evaluation in SDN-enabled fog computing is an area that has not yet been fully explored by the research community. We present a novel SDN-enabled fog architecture to improve power efficacy and performance by leveraging cooperative and noncooperative policy-based computing. Preliminary results from extensive simulation demonstrate an improvement in the power utilization as well as the overall performance (i.e., processing time, response time). Finally, we discuss several open research issues that need further investigation in the future.

Wild Animal Information Collection Based on Depthwise Separable Convolution in Software Defined IoT Networks

The wild animal information collection based on the wireless sensor network (WSN) has an enormous number of applications, as demonstrated in the literature. Yet, it has many problems, such as low information density and high energy consumption ratio. The traditional Internet of Things (IoT) system has characteristics of limited resources and task specificity. Therefore, we introduce an improved deep neural network (DNN) structure to solve task specificity. In addition, we determine a programmability idea of software-defined network (SDN) to solve the problems of high energy consumption ratio and low information density brought about by low autonomy of equipment. By introducing some advanced network structures, such as attention mechanism, residuals, depthwise (DW) convolution, pointwise (PW) convolution, spatial pyramid pooling (SPP), and feature pyramid networks (FPN), a lightweight object detection network with a fast response is designed. Meanwhile, the concept of control plane and data plane in SDN is introduced, and nodes are divided into different types to facilitate intelligent wake-up, thereby realizing high-precision detection and high information density of the detection system. The results show that the proposed scheme can improve the detection response speed and reduce the model parameters while ensuring detection accuracy in the software-defined IoT networks.

Generation and collection of data for normal and conflicting flows in software defined network flow table

<a name="_Hlk31039004"></a><span lang="EN-US">In terms of network simplification and regulation, Software Defined Networking (SDN) is a new form of infrastructure that offers greater adaptability and flexibility. SDN, however, is an invention that is logically centralized. </span><span>In addition, the optimization of the control plane and data plane in SDN has become an area deserving of more attention. The flow in OpenFlow has been one of the essential parameters in the SDN standards, in which every individual flow includes packet matching fields, flow priority, separate counters, instructions for packet forwarding, flow timeouts and a cookie. This research work is conducted in order to produce and collect flows from the OpenFlow switch in two scenarios; in normal flows and when conflict policy rules are enforced in the network. In this article, the throughput is required to review and evaluate the conflict impact on two protocols as a performance metric; the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) that flows via a forwarded plane. During the simulation of the SDN OpenFlow network, the metrics are tested using MININET. The results demonstrate that the existence of SDN conflict rules allows TCP and UDP to have a significant average change in bandwidth that eventually affects the network and operations performance.</span>

EFSUTE: a novel efficient and survivable traffic engineering for software defined networks

Computer networks are the fundamental infrastructure of intelligent environments (IEs). Software defined network (SDN) is an emerging software-based network architecture that separates the control plane from data plane to facilitate dynamic network management and configuration; two important parameters which are essential in real-time IEs. This separation alleviates network administrators to implement efficient fault tolerance strategies and increasing reliability and quality of service (QoS). Hence, they can make their networks more reliable and as a result, decrease the adverse effects due to network failures. As a result, SDN is a candidate for using in new IEs such as internet of things (IoT). The control plane of SDN can be deployed on the IoT cloud server to manage the entire network. Link failure is one of the most common and critical failures in such computer networks which causes dropping of data packets and reduces network performance considerably. A survivable traffic engineering (TE) method can provide protection against link failures and heighten the reliability of data transmission. To this purpose, this paper proposes an efficient and survivable software-based TE model over SDN called EFSUTE to increase reliability in real-time IEs. EFSUTE leverages the capabilities of SDN to compute and install two disjoint paths between any source–destination pair in the network. EFSUTE models network routing as a Shared Risk Link Group problem which provides topological diversity. This policy increases the survivability by providing alternative paths between network hosts. To prove the effectiveness of EFSUTE, we conducted extensive simulations and compared it with restoration techniques. Experimental results confirm that EFSUTE can be practically used in SDN and efficiently protects the network traffics against link failures to increase safety and provides a reliable software based infrastructure with QoS for real-time IEs.

An integrated multi-controller management framework for highly reliable software defined networking

Software-defined networking (SDN) has become the technology of choice for designing the next-generation network infrastructure that is featured with high-volume traffics, rapidly increased scale, and dynamic nature. Furthermore, to deploy multiple controllers in the control plane of SDN is widely considered with the aim of improving the stability and reliability of the network. This paper presents an integrated framework for a comprehensive multi-controller management in SDN. The proposed framework is comprised of a network planning phase and a runtime maintenance phase. Novel algorithms are proposed in the network planning phase to estimate the required number of controllers in the network, to determine the nodes for placing the controllers, and to assign the switch to its managing controller. Moreover, these algorithms are designed by mitigating the problems of device isolation and controller overload such that the reliability and stability of the control plane can be enhanced. In addition, a mechanism based on the State Behavior Tree is proposed in the runtime maintenance phase of the framework. This mechanism dynamically manages the loading of the controller during the execution time so that the occurrence of the controller overload is minimized. The experimental results show that, compared to the prior arts, the proposed framework reduces the isolation probability by up to 89% and increases the device connectivity by up to 34%. The occurrence of the controller overload during runtime is also significantly decreased.

A Hybrid SDN Switch Based on Standard P4 Code

This letter presents an enhanced hybrid Software-Defined Networking (SDN) layer-2 switch whose behavior is specified by the Programming Protocol-independent Packet Processors (P4) language. Its SDN capabilities are enabled by using P4Runtime as control plane protocol to specify the forwarding rules used by its programmable data plane. Additionally, the device is also able to exploit P4 registers for an autonomous self-definition of its forwarding capabilities, with the goal of avoiding an overload of the SDN control plane. Its performance is better than other P4 proposals based on non-standard externs and similar to other platform-dependent implementations.

DSF: A Distributed SDN Control Plane Framework for the East/West Interface

The ever-increasing number of network-capable devices places a massive burden on modern networks. Communication infrastructure should provide quality-of-service essentials in terms of high-bandwidth capacity, scalability, resiliency, and security. Programmable networks are viewed as the prevailing method of encountering the challenges introduced by the accelerated expansion. The ability of software-defined networking (SDN) to separate the control plane from the data plane and enable the programmability of the network creates new ways to architect the network. The centralization of control logic introduces complexities in large-scale, distributed networks such as performance bottlenecks and reliability. Distributed SDN controllers have been proposed to overcome the performance concerns. The lack of a communication standard among distributed controllers, referred to as the East/West interface, presents a challenge in the adoption of SDN in large-scale, distributed networks. In this paper, we propose Distributed SDN control plane Framework (DSF) - a framework for the East/West interface for heterogeneous, distributed SDN controllers to synchronize topologies using a standardized data-centric real-time publish/subscribe paradigm known as the Data Distribution Service (DDS). Distributed control plane architectures are proposed using DSF: flat, hierarchical, and T-model. The DSF interface is implemented on multiple SDN control plane platforms to evaluate performance: Floodlight and Open Network Operating System (ONOS) controllers. Test cases with different configurations are designed for performance evaluation of the proposed interface in homogeneous and heterogeneous SDN control planes. In addition, a performance comparison is presented of DSF-based ONOS controllers versus Atomix-based ONOS cluster solutions.

SDN Wireless Controller Placement Problem-The 4G LTE-U Case

To cope with the shortage of available licensed spectrum, 4th Generation Long Term Evolution (4G LTE) is expected to be deployed also in the unlicensed spectrum. This raises the problem of the coexistence of multiple operators. Obviously, a Software Defined Networking (SDN) based control approach can utilize the radio bandwidth by coordinating between multiple LTE-Unlicensed (LTE-U) operators. Within SDN control plane, there is a well-known issue namely the Controller Placement Problem (CPP) which has a major impact on the efficiency of the control plane. This paper addresses the Wireless Controller Placement Problem (WCPP) when the SouthBound interface (SBi) is based on 4G LTE unlicensed. The proposed solution improves the throughput, link failure probability and the transparency on the SBi from concurrent transmissions on the control plane. Two heuristic solutions are considered, one is a simulated annealing based and the other is a ray-shooting based. The simulation results show that they are fast and efficient and the ray-shooting based heuristic out-performs the simulated annealing based heuristic. In addition, it is shown that the transparency improves in bigger networks.

Load Balancing Algorithm for Migrating Switches in Software-defined Vehicular Networks

In Software-Defined Networks (SDN), the divergence of the control interface from the data plane provides a unique platform to develop a programmable and flexible network. A single controller, due to heavy load traffic triggered by different intelligent devices can not handle due to it’s restricted capability. To manage this, it is necessary to implement multiple controllers on the control plane to achieve quality network performance and robustness. The flow of data through the multiple controllers also varies, resulting in an unequal distribution of load between different controllers. One major drawback of the multiple controllers is their constant configuration of the mapping of the switch-controller, quickly allowing unequal distribution of load between controllers. To overcome this drawback, Software-Defined Vehicular Networking (SDVN) has evolved as a configurable and scalable network, that has quickly achieved attraction in wireless communications from research groups, businesses, and industries administration. In this paper, we have proposed a load balancing algorithm based on latency for multiple SDN controllers. It acknowledges the evolving characteristics of real-time latency <i>vs.</i> controller loads. By choosing the required latency and resolving multiple overloads simultaneously, our proposed algorithm solves the load-balancing problems with multiple overloaded controllers in the SDN control plane. In addition to the migration, our algorithm has improved 25% latency as compared to the existing algorithms.

Towards Crossfire Distributed Denial of Service Attack Protection Using Intent-Based Moving Target Defense Over Software-Defined Networking

Crossfire is an indirect target area link-flooding Distributed Denial of Service (DDoS) attack determined to affect the neighbors of the real target. Currently, Crossfire DDoS attacks are acquiring impetus because of their indistinguishability and undetectability. SDN (Software Defined Networking) is a progressing technique because of its adaptability and programmability. Moving Target Defense (MTD) is an arising security strategy to counter attacks by progressively changing the attacked plane. IBN (Intent-based Networking) is another promising methodology for providing dynamic network management. IBN-based MTD can provide efficient MTD solutions because of the concentrated control and observing capacities of the intents when translated into rules inside the SDN control plane. In this paper, a framework for the security of Crossfire DDoS attacks is proposed by making use of Intent-based Traffic modifications through the Open Networking Operating System (ONOS) Rest API and Domain Name System (DNS) port redirection. In this paper, we exploited Intent-based MTD to divert traffic from the principal host to virtual shadow hosts to counter this attack. Traffic redirection helps in masquerading the attacker headed for shadow host and consequently getting the erroneous path towards the network and, hence, the Crossfire attack couldn’t be executed as expected. The proposed technique is simulated using Mininet and ONOS SDN controllers. The outcomes showed traffic is successfully redirected at a low computational expense. Therefore, Crossfire DDoS is efficiently mitigated as promising results are found.

Quality of transmission-aware control plane performance analysis for elastic optical networks

Physical layer impairment-aware control plane techniques are studied for dynamic networking of elastic optical networks (EONs). To this end, performance of three control plane architectures, the generalized multi-protocol label switching (GMPLS), the GMPLS method with a path computation element unit, and the software-defined networking (SDN), are analyzed considering nonlinear interference (NLI) noise (consisting of self-phase and cross-phase modulation effects) and amplified spontaneous emission noise. Signal-to-noise ratio estimation of all the control plane architectures is performed by means of the well established gaussian noise model. To reduce the blocking probability caused by the NLI noise in EONs, an impairment-aware routing and spectrum assignment algorithm is proposed to ensure a required quality of transmission for requested light-paths. Moreover, transmit launch power influence on blocking probability is investigated exploiting extensive numerical simulations based on stochastic traffic modeling for the investigated control plane architectures in Pan-European and national science foundation network (NSFNET) topologies. The numerical results show nearly 50% improvement in blocking probability of the SDN control plane by applying an impairment aware scheme at traffics around 200 Erlang. Furthermore, the results indicate that the lowest blocking probability is observed at a transmit power of 0 dBm for all the control plane techniques.

Research on Traffic Optimization Scheme of SDN Network Based on ME-HMM

In this paper, a traffic optimization scheme based on Maximum Entropy- Hidden Markova Model (ME-HMM) in Software-Defined Networking (SDN) networks is proposed in order to resolve the load imbalance in SDN architecture. First, the data flow generated by the host in the SDN network was monitored, and denoising and analyzing would be carried out with the maximum entropy (ME) model to predict the network traffic for a period of time in the future, then the controller would be deployed. Next, the traffic flow was evaluated with Hidden Markova Model (HMM) and the deployment was optimized in the SDN control plane. With the feedback information, the scheme could optimize SDN network traffic and promote load balancing.

Securing SDN-Controlled IoT Networks Through Edge Blockchain

The Internet of Things (IoT) connected by software-defined networking (SDN) promises to bring great benefits to cyber-physical systems. However, the increased attack surface offered by the growing number of connected vulnerable devices and separation of SDN control and data planes could overturn the huge benefits of such a system. This article addresses the vulnerability of the trust relationship between the control and data planes. To meet this aim, we propose an edge computing-based Blockchain as a Service (BaaS), enabled by an external BaaS provider. The proposed solution provides verification of inserted flows through an efficient, edge-distributed, blockchain solution. We study two scenarios for the blockchain reward purpose: 1) information symmetry, in which the SDN operator has direct knowledge of the real effort spent by the BaaS provider and 2) information asymmetry, in which the BaaS provider controls the exposure of information regarding spent effort. The latter yields the so-called “moral hazard,” where the BaaS may claim higher than actual effort. We develop a novel mathematical model of the edge BaaS solution and propose an innovative algorithm of a fair reward scheme based on game theory that takes into account moral hazard. We evaluate the viability of our solution through analytical simulations. The results demonstrate the ability of the proposed algorithm to maximize the joint profits of the BaaS and SDN operator, i.e., maximizing the social welfare.