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Intelligent transmission control layer for efficient node management in SDN

delete2021-10-27
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H
Hamza Aldabbas *
K
Khalaf Khatatneh
DOI:10.1007/s10586-021-03449-3delete
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摘要

摘要

En 中文
The Software Defined Networking (SDN) promises exciting new networking functionality. However, there is always remains a chance of programming errors that result in unreliable data communication. The centralized programming model helps decrease bugs' probability where a single controller manages the whole network. Yet, many real-time events occur at switches and end hosts, which often affect and add a delay in the communication process. One of those events includes unannounced destination host migration after installing flow rules during receiving of data packets. Such destination host movement results in the loss of packets because controller is not aware of this recent event. Therefore, we need an efficient approach to transmit packets without any packet loss despite destination host migration. This paper proposed a design to achieve the objective as mentioned above by defining a layer named Intelligent Transmission Control Layer (ITCL). It monitors all the end hosts' connections at their specific locations and performs necessary actions whenever the connection state changes for one or multiple hosts. The controller collects information of end nodes and state change through ITCL using A star search algorithm. After that, it updates flow tables accordingly to accommodate a location-change scenario with a route-change policy. ICTL is developed on prototype-based implementation using a popular POX controller platform. By comparing ITCL with the existing solution, we conclude that our proposed approach exhibits efficient performance in terms of Packet loss, Bandwidth usage, and Network Throughput.
Keyword:
Traffic engineering
Routing
Machine learning
Software defined networking (SDN)
Intelligent transmission control layer (ITCL)
A star search algorithm
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期刊

C
Cluster Computing-The Journal of Networks Software Tools and Applications
IF:
4.1
论文数:
5.1K
被引数:
7.5K

机构

A
Al-Balqa Applied University
学者数:
1.2K
论文数: 1.2K
被引数: 1.2K
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