Name
Abstract | ||
|---|---|---|
Accurate traffic classification is essential for traffic engineering and Quality of Service (QoS) guarantee, especially in Internet of Things (IoT). Different applications have different network resource requirements, so an excellent classification algorithm can realize application awareness in traffic engineering and significantly improve QoS. Software Defined Network (SDN) with centralized controlling of network resources provides opportunities for fine-grained resource allocation. However, there are many issues when deep learning is employed in SDN, for example, sampling and classifying traffic data consume a lot of IO and computing resources of the SDN controller. In this paper, we deploy the Deep Neural Network (DNN) on Virtualized Network Function (VNF) to solve the problems of applying deep learning in SDN. The experiments show that the proposed DNN model outperforms existing traffic classification algorithm and the SDN controller can assign more appropriate route paths for different types of traffic and highly improve the network QoS. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MLSP.2018.8517088 | 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
Application-Aware,Deep-Learning,SDN,VNF | Traffic classification,Control theory,Computer science,Quality of service,Computer network,Resource allocation,Artificial intelligence,Deep learning,Artificial neural network,Software-defined networking,Traffic engineering,Machine learning | Conference |
ISSN | ISBN | Citations |
1551-2541 | 978-1-5386-5478-1 | 1 |
PageRank | References | Authors |
0.36 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu, J. | 1 | 23 | 16.58 |
| Jingyu Wang | 2 | 5 | 3.81 |
| Qi Qi | 3 | 210 | 56.01 |
| Haifeng Sun | 4 | 68 | 27.77 |
| Bo He | 5 | 43 | 13.55 |