Dynamic and adaptive multi-path routing algorithm based on software-defined network
Recently, there has been a surge of the video services over the Internet. However, service providers still have difficulties in providing high-quality video streaming due to the problem of scheduling efficiency and the wide fluctuations of end-to-end delays in the existing multi-path algorithms. To...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718805689 |
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| Summary: | Recently, there has been a surge of the video services over the Internet. However, service providers still have difficulties in providing high-quality video streaming due to the problem of scheduling efficiency and the wide fluctuations of end-to-end delays in the existing multi-path algorithms. To solve these two problems affecting video transmission quality, networks are expected to have the capability of dynamically managing the network nodes for satisfying quality-of-service requirements, which is a challenging issue for media streaming applications. Against this changing network landscape, this article proposes a dynamic and adaptive multi-path routing algorithm under three constraints (packet loss, time delay, and bandwidth) that are based on software-defined network for centralized routing computations and real-time network state updating in multimedia applications. Compared with related multi-path routing proposals, dynamic and adaptive multi-path routing makes efficient use of the latest global network state information achieved by the OpenFlow controller and calculates the optimal routes dynamically according to the real-time status information of the link. Moreover, our proposed algorithm can significantly reduce the computational overhead of the controller while completing a fine-grained flow balance. Experimental results show that dynamic and adaptive multi-path routing significantly outperforms other existing scheduling approaches in achieving a 35%–70% improvement in quality-of-service. |
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| ISSN: | 1550-1477 |