Software Defined Networking Paradigm Research Articles

A novel method for utilizing RF information from IEEE 802.11 frames in Software Defined Networks

Wireless security research using Radio Frequency (RF) data is complex and costly, often requiring expensive equipment and extensive offline processing. To make wireless research more accessible, we have integrated RF signal features with the existing IEEE 802.11 frame layer 2/3 data using the Software Defined Networking (SDN) paradigm. Combining low-cost RF hardware, a novel SDN processing method, and unique RF processing architecture has resulted in a framework that enables advanced wireless security and control research at a significantly lower cost. The method for enabling such functionality consist of the following stages:•During the demodulation process, extract Carrier Frequency Offset (CFO) and Pilot Sub-Carrier Offset from the Frequency Offset Correction section, in addition to the Vector Magnitude from the equalization section.•Append the new RF information to the wireless frame buffer and pass it to the SDR kernel driver module to further process before transferring them to a P4 application via a single API instantiation.•Compile a custom P4 application that combines the new RF features alongside higher OSI layer features to perform advanced networking, security, or control plane operations.

Hardware-Based Evaluation of Scalable and Resilient Multicast With BIER in P4

Traditional IP multicast (IPMC) maintains state per IPMC group in core devices to distribute one-to-many traffic along tree-like structures through the network. This limits its scalability because whenever subscribers of IPMC groups change, forwarding state in the core network needs to be updated. Bit Index Explicit Replication (BIER) has been proposed by the IETF for efficient transport of IPMC traffic without the need of IPMC-group-dependent state in core devices. However, legacy devices do not offer the required features to implement BIER. P4 is a programming language which follows the software-defined networking (SDN) paradigm. It provides a programmable data plane by programming the packet processing pipeline of P4 devices. The contribution of this article is threefold. First, we provide a hardware-based prototype of BIER and BIER fast reroute (BIER-FRR) which leverages packet recirculation. Our target is the P4-programmable high-performance switching ASIC Tofino; the source code is publicly available. Second, we perform an experimental evaluation, with regard to failover time and throughput, which shows that up to 100 Gb/s throughput can be obtained and that failures affect BIER forwarding for less than 1 ms. However, throughput can decrease if switch-internal packet loss occurs due to missing recirculation capacity. As a remedy, we add more recirculation capacity by turning physical ports into loopback mode. To quantify the problem, we derive a prediction model for reduced throughput whose results are in good accordance with measured values. Third, we provide a provisioning rule for recirculation ports, that is applicable to general P4 programs, to avoid switch-internal packet loss due to packet recirculation. In a case study we show that BIER requires only a few such ports under realistic mixes of unicast and multicast traffic.

Automatic Management of N × N Photonic Switch Powered by Machine Learning in Software-Defined Optical Transport

Optical networking is fast evolving towards the applications of the Software-defined Networking (SDN) paradigm down to the (Wavelength-division Multiplexing) WDM transport layer for cost-effective and flexible infrastructure management. Optical SDN requires each network element's software abstraction to enable full control by the centralized network controller. Nowadays, modern network elements, especially photonic switching systems, are developed by exploiting the fast-emerging technology of Photonic Integrated Circuit (PIC) that consists of complex fabrics of elementary units that can be driven individually using a large set of elementary controls. In this work, we focus on modeling the elementary control states of the topological structures behind PIC N ×N switches under a fully blind approach based on Machine Learning (ML) techniques. The ML agent's training and testing datasets are obtained synthetically by software simulation of the photonic switch structure. The proposed technique's scalability and accuracy are validated by considering different dimensions N and applying it to two different switching topologies: the Honey-Comb Rearrangeable Optical Switch and the Beneš network. Excellent results in terms of prediction of the control states are achieved for both of the considered topologies.

A Survey of the Main Security Issues and Solutions for the SDN Architecture

The software-defined networking (SDN) paradigm proposes the decoupling of control and data planes and a centralized software-oriented management approach based on a central controller, easing the development of new applications and services. These design principles pave the way for a more flexible, fast, and dynamic software-controlled network. However, in terms of security, the elements that comprise the SDN architecture present several vulnerabilities, which could be exploited by attackers to carry out malicious actions and thus affect the network and its services. Although for several years, some studies have already focused on identifying the weaknesses of the SDN layer structure, the nature of the attacks, and possible solutions for this paradigm, the literature contains few contributions that review and discuss this topic in an integral fashion. This paper provides a comprehensive, updated, and detailed review of the main security issues and mitigating measures for all layers and interfaces of the SDN architecture, classifying the contributions according to the STRIDE threat modeling methodology categories. Finally, this manuscript identifies, discusses, and synthesizes open challenges and future research directions in this area.

Parallel Route Optimization and Service Assurance in Energy-Efficient Software-Defined Industrial IoT Networks

In recent years, the Industrial world has been embracing new digital technology, including the internet of things (IoT) paradigm that promises revolutionizing-prospects in numerous industrial applications. However, many deployment challenges related to real-time big data analytics, service assurance, resource optimization, energy consumption, and security awareness are raised. In this work, we focus on service assurance and resource optimization, including energy consumption challenges over Industrial Internet of Things (IIoT)-based environments since the existing network routing algorithms cannot meet the strict heterogeneous quality of service (QoS) requirements of industrial communications while optimizing resources. We take advantage of the flexibility and programmability offered by the promising software-defined networking paradigm, and we propose a centralized route optimization and service assurance scheme, named ROSA, over a multi-layer programmable industrial architecture. The proposed solution supports a wide range of heterogeneous flows, such as ultra-reliable low-latency communications (URLLC) and bandwidth-sensitive services. The routing optimization problems are formulated as multi-constrained shortest path problems. The Lagrangian Relaxation approach is used to solve the . Hence, we deploy a pair of parallel routing algorithms run according to the flow type to ensure QoS requirements, efficiently allocate constrained resources, and enhance the overall network energy consumption. We conduct extensive simulations to validate the proposed ROSA scheme. The experimental results show promising performance in terms of reducing bandwidth utilization by up to 22%, end-to-end delay at least by 21%, packet loss by more than 19%, flow violation by about 16%, and energy consumption up to 14% as compared to well-known benchmarks in QoS provisioning and energy-aware routing problem.

On Deep Reinforcement Learning for Traffic Engineering in SD-WAN

The demand for reliable and efficient Wide Area Networks (WANs) from business customers is continuously increasing. Companies and enterprises use WANs to exchange critical data between headquarters, far-off business branches and cloud data centers. Many WANs solutions have been proposed over the years, such as: leased lines, Frame Relay, Multi-Protocol Label Switching (MPLS), Virtual Private Networks (VPN). Each solution positions differently in the trade-off between reliability, Quality of Service (QoS) and cost. Today, the emerging technology for WAN is Software-Defined Wide Area Networking (SD-WAN) that introduces the Software-Defined Networking (SDN) paradigm into the enterprise-network market. SD-WAN can support differentiated services over public WAN by dynamically reconfiguring in real-time network devices at the edge of the network according to network measurements and service requirements. On the one hand, SD-WAN reduces the high costs of guaranteed QoS WAN solutions (as MPLS), without giving away reliability in practical scenarios. On the other, it brings numerous technical challenges, such as the implementation of Traffic Engineering (TE) methods. TE is critically important for enterprises not only to efficiently orchestrate network traffic among the edge devices, but also to keep their services always available. In this work, we develop different kind of TE algorithms with the aim of improving the performance of an SD-WAN based network in terms of service availability. We first evaluate the performance of baseline TE algorithms. Then, we implement different deep Reinforcement Learning (deep-RL) algorithms to overcome the limitations of the baseline approaches. Specifically, we implement three kinds of deep-RL algorithms, which are: policy gradient, TD- λ and deep Q-learning. Results show that a deep-RL algorithm with a well-designed reward function is capable of increasing the overall network availability and guaranteeing network protection and restoration in SD-WAN.

An efficient approach to optimization of semi‐stable routing in multicommodity flow networks

AbstractIdeally, the network should be dynamically reconfigured as traffic evolves. Yet, even within the software defined network paradigm, network reconfigurations cannot be too frequent due to a number of reasons related to route consistency, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, and so on. In this paper, we focus on the fundamental issue of deciding whether, when, and how to reconfigure the network while traffic evolves. We consider a problem of optimizing semi‐stable routing in the capacitated multicommodity flow network when one may use at most a given maximum number of routing configurations (called routing clusters) and when each routing configuration must be used for at least a given minimum amount of time. We propose an efficient solution approach based on routing cluster generation that provides a tight lower bound on the minimum of a selected objective function (like maximum link delay or a sum of link delays) and suboptimal solutions very close to the calculated bound. The approach scales well with the size of the network.

Softwarized Industrial Deterministic Networking Based on Unmanned Aerial Vehicles

Guaranteeing network transmission is one of the most challenging issues in industrial informatization. In the industrial sites without proper networking infrastructure, by deploying unmanned aerial vehicles (UAV), transmission-oriented cyber-physical system (CPS) is an excellent candidate to exploit to provide transmission. In this article, we focus on establishing deterministic network transmission (DNT) using UAV-based CPS, complying with the principles in time sensitive network/deterministic networking in industrial internet. The software-defined networking (SDN) paradigm is adopted for UAVs-based CPS to achieve global optimization. First, we build the coordinate-based global topology view in the SDN controller to manage and control UAVs by integrating UAVs into the view and applying the network positioning method. Then, we introduce a hop-limited time synchronization approach to improve accuracy by reducing synchronization deviation. Last, based on the view, a geometric multipath generating method is proposed to enhance reliability by reducing the joint degree of multiple paths and facilitating convergence. The extensive simulation experiments show that our proposed UAV-CPS allows DNT to provide better reliability with reduced latency.

An efficient flow-based multi-level hybrid intrusion detection system for software-defined networks

Software-defined networking (SDN) is a novel networking paradigm that provides enhanced programming abilities, which can be used to solve traditional security challenges on the basis of more efficient approaches. The most important element in the SDN paradigm is the controller, which manages the flows of each correspondence forwarding element (switch or router). Flow statistics provided by the controller are considered to be useful information that can be used to develop a network-based intrusion detection system. Therefore, in this paper, we propose a 5-level hybrid classification system based on flow statistics in order to attain an improvement in the overall accuracy of the system. For the first level, we employ the k-nearest neighbor approach (kNN); for the second level, we use the extreme learning machine (ELM); and for the remaining levels, we utilize the hierarchical extreme learning machine (HELM) approach. In comparison with conventional supervised machine learning algorithms and other state-of-the-art methodologies based on the NSL-KDD benchmark dataset, the experimental study showed that our system achieves a good accuracy (84.29%), with an ability to detect new attacks that reaches 77.18%. Therefore, our approach presents an efficient approach for intrusion detection in SDNs.

SD-MIoT: A Software-Defined Networking Solution for Mobile Internet of Things

The Internet of Things (IoT) brings new potentials in humans’ everyday life activities through enabling applications with stringent communication demands, including mobile IoT applications. State-of-the-art routing protocols for the IoT, such as IPv6 routing protocol for low-power and lossy networks (RPL), have not been originally designed for such challenging applications. Recent proposals blend the software-defined networking (SDN) paradigm with IoT, enabling better-informed and bespoke routing control, matching the application requirements. In this article, we propose SD-MIoT , an open-source SDN solution for mobile IoT environments that consists of a modular SDN controller and an OpenFlow-like protocol. SD-MIoT detects passively in real-time, the network’s mobile nodes through mobility detector (MODE), an intelligent algorithm that utilizes the connectivity graph of the SDN controller. It incorporates novel mobility-aware topology discovery mechanisms, routing policies, and flow-rule establishment methods, all of them balancing control overhead with routing robustness, according to nodes’ mobility behavior detected by MODE. We provide extensive evaluation results over two realistic scenarios, further confirming the suitability of SD-MIoT for mobile IoT environments and demonstrating reliable operation in terms of successful data packet delivery with low control overhead.

Blockchain-Enabled Distributed Security Framework for Next-Generation IoT: An Edge Cloud and Software-Defined Network-Integrated Approach

The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day-to-day life. It is important to do research on security frameworks for next-generation IoT and develop state-of-the-art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication, and robustness, the blockchain technology comes out as a sustainable solution. A blockchain-enabled distributed security framework using edge cloud and software-defined networking (SDN) is presented in this article. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The SDN-enabled gateway offers dynamic network traffic flow management, which contributes to the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge cloud, and SDN paradigm.

Software-Defi Wireless Sensor Networks: A Systematic Review, Architecture and Challenges

Software-defined networking (SDN) paradigm leads to innovation and simplicity in network configuration and management. Wireless sensor networks (WSNs) are becoming an essential component of the Internet of Things (IoTs). The combination of these two models gives rise to a new paradigm: Software-defined wireless sensor networks (SDWSNs). This paper provides a systematic review on SDWSNs. The study also discusses open challenges of further research on SDWSNs.

RESDN: A Novel Metric and Method for Energy Efficient Routing in Software Defined Networks

Software-defined networking (SDN) paradigm, with the flexible and logically centralized control, enables dynamically minimizing the network energy consumption by redirecting paths of packets. However, the links and switches are designed to accommodate maximum traffic volume and their power consumption is not traffic proportional. Moreover, there exists a trade-off between energy efficiency and network performance that need to be considered together. Addressing these issues, we propose an energy efficiency metric named Ratio for Energy Saving in SDN (RESDN) that quantifies energy efficiency based on link utility intervals. We provide integer programming formulation and method for maximizing the RESDN of the network. To the best of our knowledge, RESDN approach is novel as it measures how links are profitably utilized in terms of the amount of energy they consume with respect to their utility. We analyze our approach considering various metrics of interest, and different types of SDN enabled switches. Experiments show that maximizing the RESDN value improves energy efficiency while maintaining acceptable network performance. In comparison to state-of-the-art utility-based heuristics, RESDN method achieves up to 30% better ratio for energy saving, 14.7 watts per switch power saving, 38% link saving, 2 hops decrease in average path length, 5% improved traffic proportionality.

Performance Modeling of Software-Defined Networking Paradigm in a Public Health Management System

The improvement in the management of Public Health through the public health management system (PHMS) has become a bottleneck. Although the management of a large number of patients' information has been studied, their networking aspect remains a challenge in prompt assessment of their records. With a view to bringing about an efficient management system, the Software Defined Networking (SDN) Technology came up with an interesting idea to properly manage network operation flow. The SDN technology enables the controller(s) to regulate the control flow in a logical manner through its intelligent control. This paper proposed a framework based on SDN to optimise the performance of large-scale PHMS. As a measure for better healthcare records, the proposed technology provides a reliable system to address huge patients' data request that is imminent. This article motivates experimentally, the need to despise the historical fear of new technology in PHMS as against the conventional/old practices.

A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios

The Internet of Things (IoT) has attracted much attention from the Information and Communication Technology (ICT) community in recent years. One of the main reasons for this is the availability of techniques provided by this paradigm, such as environmental monitoring employing user data and everyday objects. The facilities provided by the IoT infrastructure allow the development of a wide range of new business models and applications (e.g., smart homes, smart cities, or e-health). However, there are still concerns over the security measures which need to be addressed to ensure a suitable deployment. Distributed Denial of Service (DDoS) attacks are among the most severe virtual threats at present and occur prominently in this scenario, which can be mainly owed to their ease of execution. In light of this, several research studies have been conducted to find new strategies as well as improve existing techniques and solutions. The use of emerging technologies such as those based on the Software-Defined Networking (SDN) paradigm has proved to be a promising alternative as a means of mitigating DDoS attacks. However, the high granularity that characterizes the IoT scenarios and the wide range of techniques explored during the DDoS attacks make the task of finding and implementing new solutions quite challenging. This problem is exacerbated by the lack of benchmarks that can assist developers when designing new solutions for mitigating DDoS attacks for increasingly complex IoT scenarios. To fill this knowledge gap, in this study we carry out an in-depth investigation of the state-of-the-art and create a taxonomy that describes and characterizes existing solutions and highlights their main limitations. Our taxonomy provides a comprehensive view of the reasons for the deployment of the solutions, and the scenario in which they operate. The results of this study demonstrate the main benefits and drawbacks of each solution set when applied to specific scenarios by examining current trends and future perspectives, for example, the adoption of emerging technologies based on Cloud and Edge (or Fog) Computing.

Evaluasi Kinerja Protokol Perutean AODV dan SDGR+R pada VANET dengan Studi Kasus Pelabuhan Lembar

The Vehicular ad-hoc Network (VANET) is a subclass of Mobile ad-hoc networks (MANETs).VANET is a wireless network created from the concept of building a vehicle network (node) toexchange data information (data communication). There is a new concept technique forVANET communication used, namely the use of the concept of Software Defined Network(SDN) on VANET. For data communication between vehicles, a routing protocol required. Themost common routing protocol used on VANET since 2003 is AODV. In 2014 several studieswere using the SDN paradigm tried on VANET technology to improve the performance ofQuality of Service (QoS), one of which is a Geographic-based SDN, called SDGR in 2016.Multicast is a method of routing data on a network that allows one node or a group of nodes tocommunicate efficiently with the receiving node. The multicast concept supports one-to-manyrouting in nodes that send packet data to a group of nodes. The development of the SDGRrouting protocol using the idea of multicast technique to SDGR based on the Direction calledSDGR + R carried out in 2019. This study uses a case study of vehicle transportationsimulations in the Lamber Port area of Lombok. The simulation results knew that SDGR + Ris better than AODV in terms of service quality (QoS) at a latency of 15.58% and packet deliveryratio (PDR) of 47.78%.

Smart Hybrid SDN Approach for MPLS VPN Management and Adaptive Multipath Optimal Routing

The Virtual Private Network (VPN) service offered by the Multi-Protocol Label Switching Protocol (MPLS) is more and more solicited by clients thanks to its various advantages that it can offer especially concerning the security, the quality of service, and the flexibility. MPLS VPN relies on multiple protocols to function correctly and can be represented according to different architectures, each of which requires its own configuration. Besides, this technology is often used to interconnect a high number of sites which makes it challenging to deploy, manage, and supervise. Software Defined Network (SDN) is a paradigm, which, thanks to a centralized point (controller) can manage the MPLS VPN tunnels in an automatic, dynamic, and on-demand way between all the clients’ equipment. To manage the equipment by an SDN controller, they must be of the new generation supporting the OpenFlow protocol. The hybrid SDN paradigm allows Legacy equipment to be included in the orchestration as well, reducing OPEX and CAPEX costs. By the present work, we propose a new approach (SH-SDN) SDN Hybrid for the management of different architectures MPLS VPN based on a new graphical interface. The proposed approach allows not only to manage the MPLS VPN tunnels but also to ensure a reasonable quality of service level by performing an adaptive and an optimal load balancing across the multiple paths. The approach has been implemented and tested in a virtual test network consisting of different manufacturers (Cisco, Juniper, and HP). The results show that the SH-SDN approach offers a remarkable reduction in the MPLS VPN tunnel establishment time in a large network and a reasonable quality of service level of the transported applications through the multipath routing improved process.

Enhancing Internet of Things Security using Software-Defined Networking

Access control technologies are fundamental for addressing the security and privacy requirements of the Internet of Things (IoT). This paper proposes an access control solution for Constrained Application Protocol (CoAP)-based IoT services. The proposed solution considers a network of a single provider that interconnects various IoT endpoints. It leverages the Software-Defined Networking (SDN) paradigm and implements application aware policy enforcement at the network level. All operations are transparent to the IoT endpoints and no modifications are required to the IoT communication protocol. Furthermore, our solution is built on standard OpenFlow, hence it is realistic and it can be easily deployed to an existing network. We prove the feasibility of our solution through a proof of concept implementation using network emulation.

A Path Planning Scheme for AUV Flock-Based Internet-of-Underwater-Things Systems to Enable Transparent and Smart Ocean

As an emergent Internet-of-Underwater-Things (IoUT) system, the underwater wireless networks (UWNs), especially the autonomous underwater vehicle (AUV)-based UWNs are considered to be future of deep-sea exploration. Instead of underwater exploring or data collection based on an independent AUV, the multi-AUVs cooperative system or the AUV flock-based UWNs perform more efficiently and accurately in some particular underwater exploring tasks. In this article, we focus on improving the scalability or controllability of the AUV flock-based UWNs and utilize the paradigm of software-defined networking (SDN) to improve the flexibility and controllability of the AUV flock-based UWNs. With the proposed SDN-enabled architecture for the AUV flock-based UWNs, the UWNs are divided into three layers, and the data transmission, synchronization, and collection among the AUVs are implemented by the proposed software-defined beacon and control frameworks. By the centralized management feature of SDN, we define the concept of AUV flock and the united control model based on the artificial potential field theory. Then, we propose an exact path planning scheme for the AUV flock, especially when potential underwater obstacles or “no-go” areas are taken into account. We will show how an SDN controller can be a director/leader for the AUV flock-based UWNs to perform an exact underwater path planning mission. The simulation results show that our proposal is efficient in managing the operation of the AUV flock, especially the proposal SDN controller-guided path planning scheme performs more efficiently than the normal-distributed path planning scheme.

Collaborative QoE management using SDN

The Software-Defined Networking (SDN) paradigm offers the flexibility and programmability in the deployment and management of network services by separating the Control plane from the Data plane. Being based on network abstractions and virtualization techniques, SDN allows for simplifying the implementation of traffic engineering techniques as well as the communication among different services providers, included Internet Service Providers (ISPs) and Over The Top (OTT) providers. For these reasons, the SDN architectures have been widely used in the last years for the QoE-aware management of multimedia services.