Link Failure Emulation with Dijkstra and Bellman-Ford Algorithm in Software Defined Network Architecture (Case Study: Telkom University Topology)

2018 ◽  
Author(s):  
Anggie Nastiti ◽  
Andrian Rakhmatsyah ◽  
Muhammad Arief Nugroho
Keyword(s):  
Network Architecture ◽  
Link Failure ◽  
Software Defined Network

scholarly journals DeSI: A Decentralized Software-Defined Network Architecture for Internet eXchange Points

2021 ◽  
pp. 1-1
Author(s):  
Habib Mostafaei ◽  
Davinder Kumar ◽  
Gabriele Lospoto ◽  
Marco Chiesa ◽  
Giueseppe Di Battista
Keyword(s):  
Network Architecture ◽  
Software Defined Network

scholarly journals Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 169
Author(s):  
Sherief Hashima ◽  
Basem M. ElHalawany ◽  
Kohei Hatano ◽  
Kaishun Wu ◽  
Ehab Mahmoud Mohamed
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
D2d Communication ◽  
Neighbor Discovery ◽  
Research Directions ◽  
Fifth Generation ◽  
Research Activities ◽  
Comprehensive Survey ◽  
Network Overhead

Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS.

scholarly journals Software Defined Networks: a Comparative Study and Quality of Services Evaluation

2019 ◽  
Vol 6 (2) ◽  
pp. 181-192
Author(s):  
Herry Prasetyo Nugroho ◽  
Muhammad Irfan ◽  
Amrul Faruq
Keyword(s):  
Network Architecture ◽  
Quality Of Services ◽  
Network Architectures ◽  
Software Defined Network ◽  
Network Testing ◽  
Traffic Loads ◽  
Services Evaluation ◽  
Software Define Network ◽  
Software Emulator

Software-Defined Network (SDN) as architecture network that separates the control and forwarding functions, so that network operators and administrators can configure the networks in a simple and centrally between thousands of devices. This study is designed and evaluate the Quality of Services (QoS) performances between the two networks employed SDN-based architecture and without SDN-based. MinNet as a software emulator used as a data plane in the network Software Define Network. In this study, comparison of the value of the QoS on the network based on Software Defined Network and traditional network during the test run from the source node is investigated. Network testing by using traffic loads. Traffic loads are used starting from 20Mbps-100Mbps. The result is verified that the QoS analysis of the Software-Defined Network architecture performed better than conventional network architectures. The value of the latency delay on the Software Define Network range between 0,019-0,084ms, and with 0% packet loss when addressed the network traffics of 10-100Mbps.

Analysis of Issues in SDN Security and Solutions

2018 ◽  
pp. 217-239
Author(s):  
Ankur Dumka ◽  
Hardwari Lal Mandoria ◽  
Anushree Sah
Keyword(s):  
Network Topology ◽  
Network Architecture ◽  
Denial Of Service ◽  
Network Services ◽  
Software Defined Network ◽  
Denial Of Service Attack ◽  
Man In The Middle ◽  
Service Attack ◽  
Topology Information ◽  
The Given

The chapter surveys the analysis of all the security aspects of software-defined network and determines the areas that are prone to security attacks in the given software-defined network architecture. If the fundamental network topology information is poisoned, all the dependent network services will become immediately affected, causing catastrophic problems like host location hijacking attack, link fabrication attack, denial of service attack, man in the middle attack. These attacks affect the following features of SDN: availability, performance, integrity, and security. The flexibility in the programmability of control plane has both acted as a bane as well as a boon to SDN. Like the ARP poisoning in the legacy networks, there are several other vulnerabilities in the SDN architecture as well.

scholarly journals U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images—Case Study in the Joanópolis City, Brazil

2020 ◽  
Vol 12 (10) ◽  
pp. 1544 ◽  
Author(s):  
Fabien H. Wagner ◽  
Ricardo Dalagnol ◽  
Yuliya Tarabalka ◽  
Tassiana Y. F. Segantine ◽  
Rogério Thomé ◽  
...  
Keyword(s):  
Network Architecture ◽  
Satellite Images ◽  
Semantic Segmentation ◽  
Developed Countries ◽  
Building Extraction ◽  
Neural Network Architecture ◽  
Rgb Images ◽  
The City ◽  
Instance Segmentation

Currently, there exists a growing demand for individual building mapping in regions of rapid urban growth in less-developed countries. Most existing methods can segment buildings but cannot discriminate adjacent buildings. Here, we present a new convolutional neural network architecture (CNN) called U-net-id that performs building instance segmentation. The proposed network is trained with WorldView-3 satellite RGB images (0.3 m) and three different labeled masks. The first is the building mask; the second is the border mask, which is the border of the building segment with 4 pixels added outside and 3 pixels inside; and the third is the inner segment mask, which is the segment of the building diminished by 2 pixels. The architecture consists of three parallel paths, one for each mask, all starting with a U-net model. To accurately capture the overlap between the masks, all activation layers of the U-nets are copied and concatenated on each path and sent to two additional convolutional layers before the output activation layers. The method was tested with a dataset of 7563 manually delineated individual buildings of the city of Joanópolis-SP, Brazil. On this dataset, the semantic segmentation showed an overall accuracy of 97.67% and an F1-Score of 0.937 and the building individual instance segmentation showed good performance with a mean intersection over union (IoU) of 0.582 (median IoU = 0.694).

scholarly journals An Automatic Online Disaster Monitoring Network: Network Architecture and a Case Study Monitoring Slope Stability

2018 ◽  
Vol 14 (03) ◽  
pp. 4 ◽  
Author(s):  
Jianmin Wang ◽  
Xiaoqin Yang
Keyword(s):  
Network Architecture ◽  
Monitoring Network ◽  
Observation Data ◽  
Application Layer ◽  
Heterogeneous Wireless Sensor Networks ◽  
Development Direction ◽  
Disaster Monitoring ◽  
Study Monitoring

Geosensor networks(GSN) is an important development direction of the disaster monitoring in the future. An online automatic unattended disaster monitoring system can prevent and reduce the geology disaster to protect the safety of life and property. At present, most GSN are independent and usually service for respective community. The observations data of GSN are bigger and complex , and GSN is mostly heterogeneous wireless sensor networks. So this paper proposes a novel GSN disaster monitoring overall architecture, This architecture can seamlessly integrate sensors for long- term, remote, and near-real-time monitoring. In the architecture, there are four layers are used to collect, manage , transport and processing observation data. Among them, the data server layer applies the OGC SWE standards to integrate and share heterogeneous monitoring data. sensor metadata and observation data are packaged into a virtual sensor that are is transported from data center to application layer through Sensor Observation Service (SOS). To demonstrates the applicability of our proposed method, we use a case named PS-MDMs which are developed and deployed to support mine disaster monitoring and modeling research.

scholarly journals Mitigation of DDoS attack instigated by compromised switches on SDN controller by analyzing the flow rule request traffic

2018 ◽  
Vol 7 (2.6) ◽  
pp. 46 ◽  
Author(s):  
Sanjeetha R ◽  
Shikhar Srivastava ◽  
Rishab Pokharna ◽  
Syed Shafiq ◽  
Dr Anita Kanavalli
Keyword(s):  
Network Architecture ◽  
Flow Rule ◽  
Software Defined Network ◽  
Ddos Attacks ◽  
Control Plane ◽  
Ddos Attack ◽  
Flow Table ◽  
Data Plane ◽  
Ddos Detection ◽  
Sdn Controller

Software Defined Network (SDN) is a new network architecture which separates the data plane from the control plane. The SDN controller implements the control plane and switches implement the data plane. Many papers discuss about DDoS attacks on primary servers present in SDN and how they can be mitigated with the help of controller. In our paper we show how DDoS attack can be instigated on the SDN controller by manipulating the flow table entries of switches, such that they send continuous requests to the controller and exhaust its resources. This is a new, but one of the possible way in which a DDoS attack can be performed on controller. We show the vulnerability of SDN for this kind of attack. We further propose a solution for mitigating it, by running a DDoS Detection module which uses variation of flow entry request traffic from all switches in the network to identify compromised switches and blocks them completely.

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