Network function virtualization (NFV) has drawn significant attention from both industry and academia as an important shift in telecommunication service provisioning. By decoupling network functions (NFs) from the physical devices on which they run, NFV has the potential to lead to significant reductions in operating expenses (OPEX) and capital expenses (CAPEX) and facilitate the deployment of new services with increased agility and faster time-to-value. The NFV paradigm is still in its infancy and there is a large spectrum of opportunities for the research community to develop new architectures, systems and applications, and to evaluate alternatives and trade-offs in developing technologies for its successful deployment. In this paper, after discussing NFV and its relationship with complementary fields of software defined networking (SDN) and cloud computing, we survey the state-of-the-art in NFV, and identify promising research directions in this area. We also overview key NFV projects, standardization efforts, early implementations, use cases, and commercial products.

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Network Function Virtualization: State-of-the-Art and Research Challenges

Distributed denial of service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking (SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.

Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges

In recent years, there have been great advances in industrial Internet of Things (IIoT) and its related domains, such as industrial wireless networks (IWNs), big data, and cloud computing These emerging technologies will bring great opportunities for promoting industrial upgrades and even allow the introduction of the fourth industrial revolution, namely, Industry 40 In the context of Industry 40, all kinds of intelligent equipment (eg, industrial robots) supported by wired or wireless networks are widely adopted, and both real-time and delayed signals coexist Therefore, based on the advancement of software-defined networks technology, we propose a new concept for industrial environments by introducing software-defined IIoT in order to make the network more flexible In this paper, we analyze the IIoT architecture, including physical layer, IWNs, industrial cloud, and smart terminals, and describe the information interaction among different devices Then, we propose a software-defined IIoT architecture to manage physical devices and provide an interface for information exchange Subsequently, we discuss the prominent problems and possible solutions for software-defined IIoT Finally, we select an intelligent manufacturing environment as an assessment test bed, and implement the basic experimental analysis This paper will open a new research direction of IIoT and accelerate the implementation of Industry 40

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Software-Defined Industrial Internet of Things in the Context of Industry 4.0

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Software defined networking (SDN) brings about innovation, simplicity in network management, and configuration in network computing. Traditional networks often lack the flexibility to bring into effect instant changes because of the rigidity of the network and also the over dependence on proprietary services. SDN decouples the control plane from the data plane, thus moving the control logic from the node to a central controller. A wireless sensor network (WSN) is a great platform for low-rate wireless personal area networks with little resources and short communication ranges. However, as the scale of WSN expands, it faces several challenges, such as network management and heterogeneous-node networks. The SDN approach to WSNs seeks to alleviate most of the challenges and ultimately foster efficiency and sustainability in WSNs. The fusion of these two models gives rise to a new paradigm: Software defined wireless sensor networks (SDWSN). The SDWSN model is also envisioned to play a critical role in the looming Internet of Things paradigm. This paper presents a comprehensive review of the SDWSN literature. Moreover, it delves into some of the challenges facing this paradigm, as well as the major SDWSN design requirements that need to be considered to address these challenges.

A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements

Software-defined network (SDN) has become one of the most important architectures for the management of largescale complex networks, which may require repolicing or reconfigurations from time to time. SDN achieves easy repolicing by decoupling the control plane from data plane. Thus, the network routers/switches just simply forward packets by following the flow table rules set by the control plane. Currently, OpenFlow is the most popular SDN protocol/standard and has a set of design specifications. Although SDN/OpenFlow is a relatively new area, it has attracted much attention from both academia and industry. In this paper, we will conduct a comprehensive survey of the important topics in SDN/OpenFlow implementation, including the basic concept, applications, language abstraction, controller, virtualization, quality of service, security, and its integration with wireless and optical networks. We will compare the pros and cons of different schemes and discuss the future research trends in this exciting area. This survey can help both industry and academia R&D people to understand the latest progress of SDN/OpenFlow designs.

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A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation

Charging infrastructure is an important component for the healthy growth of the electric vehicle industry. This article presents an energy management and control study of an electric vehicle charging station. The charging station consists of an AC/DC converter for grid interface and multiple DC/DC converters for electric vehicle battery management. For the grid-side AC/DC converter, a direct-current control mechanism is employed for reactive power, AC system bus voltage, and DC-link voltage control. For the electric vehicle-side DC/DC converters, constant current and constant voltage control mechanisms are developed for electric vehicle charging and discharging management. The article considers energy management needs for charge and discharge of multiple electric vehicles simultaneously in a dynamic price framework. A real-time simulation system is developed to evaluate how the electric vehicle charging station can meet grid-to-vehicle, vehicle-to-grid, and vehicle-to-vehicle charging and dischar...

Energy Management and Control of Electric Vehicle Charging Stations

Electric drive vehicles (EDVs) have got many benefits as compared to normal petrol or gas cars. Moreover, the electrification of transportation systems would enable increased electricity generation from carbon-free and renewable energy sources, such as wind, solar, and hydro. However, due to highly distributed and mobile nature as well as high charge and discharge power demand of EDVs, it is important to investigate how to manage EDV charge and discharge to enhance the usage of renewable resources in the future smart grid framework. This paper presents an energy control study in a charging station, a typical integrated EDV and utility system. The charging station consists of an ac/dc converter for grid interface and multiple dc/dc converters for EDV battery management. For the grid-side converter, a direct-current control mechanism is employed for reactive power, ac system bus voltage, and dc-link voltage control. For the EDV-side converters, constant-current and constant voltage control mechanisms are developed for charging and discharging control. The paper considers energy management need for charge and discharge of multiple EDVs simultaneously as well as V2G and G2V requirements. A realtime simulation system is developed and the performance of the integrated EDV and utility system is investigated.

Battery charge and discharge control for energy management in EV and utility integration

In the software defined networking (SDN)-based wireless mesh network (WMN) architecture (SD-WMN), the network is monitored and managed in a centralized manner. Although the use of SDN simplifies the WMN management, it is still not clear whether the SDN architecture, which was originally designed for wired links, can effectively handle the dynamic topology of WMNs. This paper addresses the traffic balancing issue introduced by node mobility. To reduce the response time of SDN controller for dynamic network topology, a two-layer supervised learning model is proposed that helps the controller to predict the node mobility and link failure probability. Then, an alternative route selection scheme is proposed, which achieves optimal traffic balancing while minimizing the control plane overhead. Efficiency of the proposed SD-WMN architecture is validated in the widely used network simulator—ns-3. Our results demonstrate the network throughput enhancement achieved by the proposed SD-WMN architecture with link-failure adaptive traffic control.

Intelligent Software-Defined Mesh Networks With Link-Failure Adaptive Traffic Balancing

Solar photovoltaic (PV) energy is becoming an increasingly important part of the world's renewable energy. In order to develop efficient technology for effective energy extraction from a solar PV system, this paper investigates and compares typical maximum power point tracking (MPPT) control strategies used in solar PV industry. This paper focuses especially on how sampling rate and time delay influence PV system performance by using different MPPT control approaches in a digital control environment, and how different MPPT techniques behave under highly dynamic and variable weather conditions. A computational experiment system is developed by using MatLab, SimPowerSystems and Opal-RT real-time simulation system for fast and accurate performance evaluations of PV arrays under high frequency switching conditions of power converters. Advantages, disadvantages, and characteristics of different MPPT techniques are studied, evaluated, and compared through extensive computational experiments.

Ke Bao

Papers

A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation

Energy Management and Control of Electric Vehicle Charging Stations

Battery charge and discharge control for energy management in EV and utility integration

Intelligent Software-Defined Mesh Networks With Link-Failure Adaptive Traffic Balancing

Comparative study of maximum power point tracking control strategies for solar PV systems