scholarly journals Software-defined networking control plane for seamless integration of multiple silicon photonic switches in Datacom networks

2018 ◽  
Vol 26 (8) ◽  
pp. 10914 ◽  
Author(s):  
Yiwen Shen ◽  
Maarten H. N. Hattink ◽  
Payman Samadi ◽  
Qixiang Cheng ◽  
Ziyiz Hu ◽  
...  
Keyword(s):  
Software Defined Networking ◽  
Control Plane ◽  
Seamless Integration ◽  
Photonic Switches ◽  
Silicon Photonic

Software-Defined Networking (SDN) and 5G Network: The Role of Controller Placement for Scalable Control Plane

2020 ◽  
Author(s):  
Sharifah K. Syed-Yusof ◽  
Paulson Eberechukwu Numan ◽  
Kamaludin Mohamad Yusof ◽  
Jafri Bin Din ◽  
Muhammad Nadzir Bin Marsono ◽  
...  
Keyword(s):  
Software Defined Networking ◽  
Control Plane ◽  
5G Network ◽  
Controller Placement

An intelligent flow-based and signature-based IDS for SDNs using ensemble feature selection and a multi-layer machine learning-based classifier

2020 ◽  
pp. 1-20
Author(s):  
K. Muthamil Sudar ◽  
P. Deepalakshmi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Network Architecture ◽  
Detection System ◽  
Software Defined Networking ◽  
Control Plane ◽  
Control Logic ◽  
Data Plane

Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane.

scholarly journals Tapless and topology agnostic calibration solution for silicon photonic switches

2018 ◽  
Vol 26 (25) ◽  
pp. 32662 ◽  
Author(s):  
Alexander Gazman ◽  
Evgeny Manzhosov ◽  
Colm Browning ◽  
Meisam Bahadori ◽  
Yanir London ◽  
...  
Keyword(s):  
Photonic Switches ◽  
And Topology ◽  
Silicon Photonic ◽  
Calibration Solution

scholarly journals Load Balancing in Software-Defined Networking using Controller Placement

2020 ◽  
Author(s):  
Hamid Nejadnik ◽  
Rasool Sadeghi ◽  
Sayed Mahdi Faghih Imani
Keyword(s):  
Load Balancing ◽  
Software Defined Networking ◽  
Network Simulator ◽  
Control Plane ◽  
Placement Problem ◽  
Data Plane ◽  
Controller Placement ◽  
Controller Placement Problem ◽  
Load Balancer

Abstract Software Defined Networking (SDN) is a novel architecture that separates the data plane from the control plane using an external controller. Similar to traditional networks, load balancing has a great impact on the performance and availability of SDN. Therefore, the Controller Placement Problem (CPP) in SDN influences on the load balancing solutions. In this paper, various topologies of CPP including different load balancer controllers are simulated and evaluated in the SDN using the OFSwitch13 module of ns-3 network simulator. The results provide a solid comparison of the proposed topologies in different network situations.

A distributed load balancing algorithm for the control plane in software defined networking

2016 ◽  
Author(s):  
Federico Cimorelli ◽  
Francesco Delli Priscoli ◽  
Antonio Pietrabissa ◽  
Lorenzo Ricciardi Celsi ◽  
Vincenzo Suraci ◽  
...  
Keyword(s):  
Load Balancing ◽  
Software Defined Networking ◽  
Control Plane ◽  
Distributed Load ◽  
Load Balancing Algorithm

scholarly journals Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey

2020 ◽  
Vol 12 (9) ◽  
pp. 147 ◽  
Author(s):  
Babangida Isyaku ◽  
Mohd Soperi Mohd Zahid ◽  
Maznah Bte Kamat ◽  
Kamalrulnizam Abu Bakar ◽  
Fuad A. Ghaleb
Keyword(s):  
Large Scale ◽  
Network Performance ◽  
Software Defined Networking ◽  
Cloud Services ◽  
Future Research ◽  
Control Plane ◽  
Processing Load ◽  
Flow Table ◽  
Data Plane ◽  
Large Scale Networks

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

scholarly journals Artificial Intelligence Enabled Routing in Software Defined Networking

2020 ◽  
Vol 10 (18) ◽  
pp. 6564 ◽  
Author(s):  
Yan-Jing Wu ◽  
Po-Chun Hwang ◽  
Wen-Shyang Hwang ◽  
Ming-Hua Cheng
Keyword(s):  
Artificial Intelligence ◽  
Performance Metrics ◽  
Packet Delay ◽  
Software Defined Networking ◽  
Dynamic Routing ◽  
Learning Ability ◽  
Control Plane ◽  
Monitoring Period ◽  
Data Plane ◽  
The Impact

Software defined networking (SDN) is an emerging networking architecture that separates the control plane from the data plane and moves network management to a central point, called the controller. The controller is responsible for preparing the flow tables of each switch in the data plane. Although dynamic routing can perform rerouting in case of congestion by periodically monitoring the status of each data flow, problems concerning a suitable monitoring period duration and lack of learning ability from past experiences to avoid similar but ineffective route decisions remain unsolved. This paper presents an artificial intelligence enabled routing (AIER) mechanism with congestion avoidance in SDN, which can not only alleviate the impact of monitoring periods with dynamic routing, but also provide learning ability and superior route decisions by introducing artificial intelligence (AI) technology. We evaluate the performance of the proposed AIER mechanism on the Mininet simulator by installing three additional modules, namely, topology discovery, monitoring period, and an artificial neural network, in the control plane. The effectiveness and superiority of our proposed AIER mechanism are demonstrated by performance metrics, including average throughput, packet loss ratio, and packet delay versus data rate for different monitoring periods in the system.

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