A tensor-based big data model for QoS improvement in software defined networks
The growing volume of network traffic and gradual deployment of SDN devices initiate a new era in which one distinguished feature is the application of big data technology to SDNs for construction of flexible, scalable, and self-managing networks. The primary purpose of this article is to develop a...
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| Veröffentlicht in: | IEEE network 2016-01, Vol.30 (1), p.30-35 |
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| creator | Liwei Kuang Yang, Laurence T. Xiaokang Wang Puming Wang Yaliang Zhao |
| description | The growing volume of network traffic and gradual deployment of SDN devices initiate a new era in which one distinguished feature is the application of big data technology to SDNs for construction of flexible, scalable, and self-managing networks. The primary purpose of this article is to develop a novel tensor-based model for efficient provisioning of QoS in software defined networks. First, a forwarding tensor model is proposed to formalize the networking functions in the data plane; then a controlling tensor model is presented for routing path recommendation in the control plane. Finally, the article introduces a transition tensor model for network traffic prediction and QoS provisioning. The three models can automatically monitor the network state, recommend routing paths and predict network traffic, respectively. A case study to recommend routing paths is investigated in the article. |
| doi_str_mv | 10.1109/MNET.2016.7389828 |
| format | Article |
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| source | IEEE Electronic Library (IEL) |
| subjects | Big Data Computer networks Data models IP networks Mathematical analysis Mathematical models Networks Predictive models Quality of service Quality of service architectures Routing Software Software defined networks Tensors Traffic engineering Traffic flow |
| title | A tensor-based big data model for QoS improvement in software defined networks |
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