Paper Info
Title
NNIRSS: neural network-based intelligent routing scheme for SDN
Abstract
With the increasing diversification of network applications, SDN tends to be inefficient to satisfy the diversified application demands. Meanwhile, the continuous update of OpenFlow and flow table expansion causes the efficiency of routing and forwarding ability decreased as well as the storage space of ternary content addressable memory (TCAM) occupied by flow tables increased. In this paper, we present NNIRSS, a novel neural network (NN)-based intelligent routing scheme for SDN, which leverages the centralized controller to achieve transmission patterns of data flow by utilizing NN and replaces flow table with well-trained NN in the form of NN packet. The route of data flow can be predicted based on its application type to meet the quality of service requirements of network applications. Furthermore, we devise a radial basis function neural network-based intelligent routing mechanism. With combining APC-III and K-means algorithm, we propose APC-K-means algorithm to determine radial basis function centers. Finally, the simulation results demonstrate that our proposed NNIRSS is feasible and effective. It can reduce storage space of TCAM and routing time overhead as well as improve routing efficiency.
Year
DOI
Venue
2019
10.1007/s00521-018-3427-z
Neural Computing and Applications
Keywords
Field
DocType
SDN, Intelligent routing, RBFNN, APC-K-means algorithm
Control theory,Mathematical optimization,Content-addressable memory,Radial basis function,Network packet,Quality of service,OpenFlow,Artificial neural network,Mathematics,Distributed computing,Data flow diagram
Journal
Volume
Issue
ISSN
31.0
10
1433-3058
Citations 
PageRank 
References 
0
0.34
22
Authors
4
Name
Order
Citations
PageRank
Chuangchuang Zhang123.74
Xingwei Wang21025154.16
Fuliang Li3187.12
Min Huang44011.73