A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks

SDN has been regarded as the next-generation network paradigm as it decouples complex network management from packet forwarding, which significantly simplifies the operation of switches in the data plane. The good programmability of SDN infrastructure also improves network feasibility. To alleviate...

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Veröffentlicht in:	IEEE wireless communications Jg. 25; H. 4; S. 74 - 81
Hauptverfasser:	Mao, Bomin, Tang, Fengxiao, Fadlullah, Zubair Md, Kato, Nei, Akashi, Osamu, Inoue, Takeru, Mizutani, Kimihiro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Communications traffic Computer architecture Control systems Deep learning Machine learning Network management systems Protocol (computers) Routing protocols Supervised learning Switches Switching theory Traffic control Training data Wireless networks
ISSN:	1536-1284, 1558-0687
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Abstract	SDN has been regarded as the next-generation network paradigm as it decouples complex network management from packet forwarding, which significantly simplifies the operation of switches in the data plane. The good programmability of SDN infrastructure also improves network feasibility. To alleviate the burden of the explosive growth in network traffic, in this article we propose a non-supervised deep learning based routing strategy running in the SDN controller. In our proposal, we utilize the CNNs as our deep learning architecture, and the controller runs the CNNs to choose the best path combination for packet forwarding in switches. More importantly, in our proposal, the controller collects the network traffic trace and periodically trains the CNNs to adapt them to the changing traffic patterns. Simulation results demonstrate that our proposal is able to retain learning from previous experiences and outperform conventional routing protocols.
AbstractList	SDN has been regarded as the next-generation network paradigm as it decouples complex network management from packet forwarding, which significantly simplifies the operation of switches in the data plane. The good programmability of SDN infrastructure also improves network feasibility. To alleviate the burden of the explosive growth in network traffic, in this article we propose a non-supervised deep learning based routing strategy running in the SDN controller. In our proposal, we utilize the CNNs as our deep learning architecture, and the controller runs the CNNs to choose the best path combination for packet forwarding in switches. More importantly, in our proposal, the controller collects the network traffic trace and periodically trains the CNNs to adapt them to the changing traffic patterns. Simulation results demonstrate that our proposal is able to retain learning from previous experiences and outperform conventional routing protocols.
Author	Tang, Fengxiao Mizutani, Kimihiro Kato, Nei Fadlullah, Zubair Md Akashi, Osamu Mao, Bomin Inoue, Takeru
Author_xml	– sequence: 1 givenname: Bomin surname: Mao fullname: Mao, Bomin – sequence: 2 givenname: Fengxiao surname: Tang fullname: Tang, Fengxiao – sequence: 3 givenname: Zubair Md surname: Fadlullah fullname: Fadlullah, Zubair Md – sequence: 4 givenname: Nei surname: Kato fullname: Kato, Nei – sequence: 5 givenname: Osamu surname: Akashi fullname: Akashi, Osamu – sequence: 6 givenname: Takeru surname: Inoue fullname: Inoue, Takeru – sequence: 7 givenname: Kimihiro surname: Mizutani fullname: Mizutani, Kimihiro
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SubjectTerms	Communications traffic Computer architecture Control systems Deep learning Machine learning Network management systems Protocol (computers) Routing protocols Supervised learning Switches Switching theory Traffic control Training data Wireless networks
Title	A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks
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