Software-Defined Networking (SDN)

2021 ◽  
pp. 242-250
Author(s):  
L. Naga Durgaprasad Reddy
Keyword(s):  
Resource Allocation ◽  
Network Management ◽  
Software Defined Networking ◽  
Dynamic Resource Allocation ◽  
Control Plane ◽  
Application Layer ◽  
Physical Network ◽  
Data Plane ◽  
Dynamic Resource ◽  
Virtual Services

This chapter researches in the area of software-defined networking. Software-defined networking was developed in an attempt to simplify networking and make it more secure. By separating the control plane (the controller)—which decides where packets are sent—from the data plane (the physical network)—which forwards traffic to its destination—the creators of SDN hoped to achieve scalability and agility in network management. The application layer (virtual services) is also separate. SDN increasingly uses elastic cloud architectures and dynamic resource allocation to achieve its infrastructure goals.

scholarly journals A Review of the Control Plane Scalability Approaches in Software Defined Networking

2020 ◽  
Vol 12 (3) ◽  
pp. 49
Author(s):  
Abdelrahman Abuarqoub
Keyword(s):  
Network Management ◽  
Software Defined Networking ◽  
Research Trends ◽  
Control Plane ◽  
Recent Advances ◽  
Data Plane ◽  
Significant Research ◽  
Flexible Networks ◽  
Sdn Controller

Recent advances in information and communications cloud-based services hold the potential to overcome the scalability and complex maintenance limitations of traditional networks. Software Defined Networking (SDN) surfaced as a promising paradigm to mitigate such limitations while offering flexible networks management. Particularly, SDN separates the control plane from the data plane to achieve abstraction of lower-level functionality, hence, allowing more efficient network management and utilization. However, SDN suffers from various performance and scalability problems leading to significant research efforts on maximizing the scalability of the control plane. This paper aims at reviewing different SDN controller scalability, topology-based and mechanism-based approaches, as well as discussing and analyzing how they attempt to solve the scalability challenge. Furthermore, this paper elaborates on the promising research trends and challenges. Our insights are also discussed to stimulate further research efforts addressing the control plane scalability in SDN.

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.

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