Artificial Intelligence Enabled Routing in Software Defined Networking
Software defined networking (SDN) is an emerging networking architecture that separates the control plane from the data plane and moves network management to a central point, called the controller. The controller is responsible for preparing the flow tables of each switch in the data plane. Although...
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doaj-2ea9e7cc345e408d8102d0604e39feeb2020-11-25T03:02:53ZengMDPI AGApplied Sciences2076-34172020-09-01106564656410.3390/app10186564Artificial Intelligence Enabled Routing in Software Defined NetworkingYan-Jing Wu0Po-Chun Hwang1Wen-Shyang Hwang2Ming-Hua Cheng3Department of Information Technology and Communication, Shih Chien University, Kaohsiung Campus, Kaohsiung 845, TaiwanDepartment of Electrical Engineering, National Kaohsiung University of Science and Technology, Jiangong Campus, Kaohsiung 807, TaiwanDepartment of Electrical Engineering, National Kaohsiung University of Science and Technology, Jiangong Campus, Kaohsiung 807, TaiwanDepartment of Digital Media Design, Tzu-Hui Institute of Technology, Pingtung 926, TaiwanSoftware defined networking (SDN) is an emerging networking architecture that separates the control plane from the data plane and moves network management to a central point, called the controller. The controller is responsible for preparing the flow tables of each switch in the data plane. Although dynamic routing can perform rerouting in case of congestion by periodically monitoring the status of each data flow, problems concerning a suitable monitoring period duration and lack of learning ability from past experiences to avoid similar but ineffective route decisions remain unsolved. This paper presents an artificial intelligence enabled routing (AIER) mechanism with congestion avoidance in SDN, which can not only alleviate the impact of monitoring periods with dynamic routing, but also provide learning ability and superior route decisions by introducing artificial intelligence (AI) technology. We evaluate the performance of the proposed AIER mechanism on the Mininet simulator by installing three additional modules, namely, topology discovery, monitoring period, and an artificial neural network, in the control plane. The effectiveness and superiority of our proposed AIER mechanism are demonstrated by performance metrics, including average throughput, packet loss ratio, and packet delay versus data rate for different monitoring periods in the system.https://www.mdpi.com/2076-3417/10/18/6564software defined networkingartificial neural networkroutingmonitoring period |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yan-Jing Wu Po-Chun Hwang Wen-Shyang Hwang Ming-Hua Cheng |
spellingShingle |
Yan-Jing Wu Po-Chun Hwang Wen-Shyang Hwang Ming-Hua Cheng Artificial Intelligence Enabled Routing in Software Defined Networking Applied Sciences software defined networking artificial neural network routing monitoring period |
author_facet |
Yan-Jing Wu Po-Chun Hwang Wen-Shyang Hwang Ming-Hua Cheng |
author_sort |
Yan-Jing Wu |
title |
Artificial Intelligence Enabled Routing in Software Defined Networking |
title_short |
Artificial Intelligence Enabled Routing in Software Defined Networking |
title_full |
Artificial Intelligence Enabled Routing in Software Defined Networking |
title_fullStr |
Artificial Intelligence Enabled Routing in Software Defined Networking |
title_full_unstemmed |
Artificial Intelligence Enabled Routing in Software Defined Networking |
title_sort |
artificial intelligence enabled routing in software defined networking |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-09-01 |
description |
Software defined networking (SDN) is an emerging networking architecture that separates the control plane from the data plane and moves network management to a central point, called the controller. The controller is responsible for preparing the flow tables of each switch in the data plane. Although dynamic routing can perform rerouting in case of congestion by periodically monitoring the status of each data flow, problems concerning a suitable monitoring period duration and lack of learning ability from past experiences to avoid similar but ineffective route decisions remain unsolved. This paper presents an artificial intelligence enabled routing (AIER) mechanism with congestion avoidance in SDN, which can not only alleviate the impact of monitoring periods with dynamic routing, but also provide learning ability and superior route decisions by introducing artificial intelligence (AI) technology. We evaluate the performance of the proposed AIER mechanism on the Mininet simulator by installing three additional modules, namely, topology discovery, monitoring period, and an artificial neural network, in the control plane. The effectiveness and superiority of our proposed AIER mechanism are demonstrated by performance metrics, including average throughput, packet loss ratio, and packet delay versus data rate for different monitoring periods in the system. |
topic |
software defined networking artificial neural network routing monitoring period |
url |
https://www.mdpi.com/2076-3417/10/18/6564 |
work_keys_str_mv |
AT yanjingwu artificialintelligenceenabledroutinginsoftwaredefinednetworking AT pochunhwang artificialintelligenceenabledroutinginsoftwaredefinednetworking AT wenshyanghwang artificialintelligenceenabledroutinginsoftwaredefinednetworking AT minghuacheng artificialintelligenceenabledroutinginsoftwaredefinednetworking |
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1724687851904303104 |