VNF Chain Placement for Large Scale IoT of Intelligent Transportation
With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/14/3819 |
| _version_ | 1797563017959833600 |
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| author | Xing Wu Jing Duan Mingyu Zhong Peng Li Jianjia Wang |
| author_facet | Xing Wu Jing Duan Mingyu Zhong Peng Li Jianjia Wang |
| author_sort | Xing Wu |
| collection | DOAJ |
| description | With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation. |
| first_indexed | 2024-03-10T18:36:35Z |
| format | Article |
| id | doaj.art-75dd8b4429294d4aae4bcafbaa3771dd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:36:35Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-75dd8b4429294d4aae4bcafbaa3771dd2023-11-20T06:13:37ZengMDPI AGSensors1424-82202020-07-012014381910.3390/s20143819VNF Chain Placement for Large Scale IoT of Intelligent TransportationXing Wu0Jing Duan1Mingyu Zhong2Peng Li3Jianjia Wang4School of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Science and Engineering, University of Aizu, Fukushima 965-8580, JapanSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaWith the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.https://www.mdpi.com/1424-8220/20/14/3819virtual network functionplacementborder nodesubgraphintelligent transportation |
| spellingShingle | Xing Wu Jing Duan Mingyu Zhong Peng Li Jianjia Wang VNF Chain Placement for Large Scale IoT of Intelligent Transportation Sensors virtual network function placement border node subgraph intelligent transportation |
| title | VNF Chain Placement for Large Scale IoT of Intelligent Transportation |
| title_full | VNF Chain Placement for Large Scale IoT of Intelligent Transportation |
| title_fullStr | VNF Chain Placement for Large Scale IoT of Intelligent Transportation |
| title_full_unstemmed | VNF Chain Placement for Large Scale IoT of Intelligent Transportation |
| title_short | VNF Chain Placement for Large Scale IoT of Intelligent Transportation |
| title_sort | vnf chain placement for large scale iot of intelligent transportation |
| topic | virtual network function placement border node subgraph intelligent transportation |
| url | https://www.mdpi.com/1424-8220/20/14/3819 |
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