ARDefense: DDoS detection and prevention using NFV and SDN

Network Function Virtualization or NFV gives numerous advantages over the conventional networking techniques by incorporating distinctive features of a network over the virtual machine (VM). It decreases capital and operational costs to give more noteworthy adaptability and flexibility. But all of these advantages come at the expense of the intrinsic system vulnerabilities because of specific sorts of cyber attacks like the Distributed Denial of Service (DDoS) attack. With the increased number of layers in NFV, it becomes easier for an attacker to execute DDoS attack. This study indicates a new model for mitigating the effects of DDoS attacks on NFV. The model has been designed specifically for the individual users especially gamers and online streamers who become victim of DDoS attack on adaily basis. However, the method can be used for a online service like a website in general as well after making certain changes which have been discussed in detail. ARDefense usually performs server migration and IP spoofing when it detects a DDoS attack on the application layer. Effectiveness of ARDefense was tested by measuring load migration and IP spoofing processing time. © 2020 IEEE

Approaches for multi-tier cloud structure management

The number of IoT devices is constantly increasing every year. The volume of generated traffic is also increasing. Therefore, the new generation of 5G networks uses technology to reduce load and reverse delays-MEC. The MEC architecture is based on a multi-tier cloud structure, but official international documents do not define the principle at which architecture level a cloud computing is selected to manage the processing of user data. Therefore, in this article, we propose comparing three architectures for managing the choice of a cloud computing. It is proposed to implement a multi-tier cloud structure on the OpenStack platform. © 2019 IEEE

Development of Edge Computing Distribution Method in VANET Based Real-Time Systems

This paper describes VANET network architecture based on SDN/MEC systems to reduce network load and traffic density. In this work, we examined the possibility of temporarily placing the application to the RSU for reducing the load on the network backhaul. The condition for temporary placement of the application is based on data on congestion of road sections from Google Map & Yandex monitoring systems and current Internet traffic statistics. The proposed architecture allows optimal use of the RSU/MEC resources and significantly reduces the load on the backhaul and Latency. © 2019 IEEE

Mobile Edge Computing for Video Application Migration

Modern telecommunication networks show steady growth in the digital cable television and IPTV market. In today’s telecommunication networks most of the services are related to transmission of video traffic. For client’s loyalty operators are busy looking for new video delivery optimization methods with the appropriate quality of experience. MEC or Mobile edge computing offers significant advantages for example, enabling operators to bring applications and content closer to the network edge i.e. close to the video content end consumer and other advantages. This is particularly interesting in video delivery, where latency negatively affect video quality. Users can receive video content with minimum delays and operators can realize operational and cost efficiencies while reducing network latency and, ultimately improving the end consumer’s quality of experience. In this paper, a video content application migration method using edge-computing technology is proposed. The proposed method is applied in a managed mode over a Software-Defined Networking (SDN); what improves the efficiency of video traffic delivery. A new concept “Exchange” is introduced for flexible and automated interaction between video delivery chain members, i.e. the network operator, the content provider and the end user. © 2019, Springer Nature Switzerland AG

Delay tolerant network model based on D2D communication

Heterogeneous networks is the basis for 5G mobile communication networks evolution, one of the major challenges is the interaction between the devices in Het-Nets without the base station. This article discusses perspectives, challenges and services 5G networks, as well as direct device-to-device communication - technology, which can provide low power, high data rate and low latency services between end-users. In this paper, we consider only the interaction process of a network node with a mobile node. Data delivering process over the network is the subject of further research.The simulation results showed that the interaction process of ad hoc network nodes in the presence of mobile nodes is random, and data transfer probability from fixed to mobile nodes depends on the mobile node moving speed. © 2019 IEEE

Intelligent System Architecture for Smart City and its Applications Based Edge Computing

There is no doubt that smart city applications dramatically increase and the need for smart cities in our modern life becomes a demand. Smart cities will enable various applications and introduce many market innovations. However, the dramatic increase in wireless devices and network traffic puts many constraints in designing such networks. To this end, we provide to develop a reliable smart city system that enables various heterogeneous applications and provides the communication infrastructure for the expected enormous number of wireless devices. The proposed system is scalable so that the increase in network traffic will be supported with no degradation in network performances. The system deploys edge computing servers and artificial intelligence. In this study, we simulated a model for the structure of a smart city based on heterogeneous edge computing and defined evaluation parameters. Finally, according to the analysis of the results will be developed a prototype for the practical implementation of the selected method using a specific example is based on a neural network algorithm to generate forecasts of Internet of Things traffic activity. © 2020 IEEE