Adaptive Scaling of Virtualized Network Function Resource Capacity

Quan YUAN; Wei YOU; Xinsheng JI; Hongbo TANG

doi:10.11999/JET200110

Volume 43 Issue 7

Jul. 2021

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Quan YUAN, Wei YOU, Xinsheng JI, Hongbo TANG. Adaptive Scaling of Virtualized Network Function Resource Capacity[J]. Journal of Electronics & Information Technology, 2021, 43(7): 1841-1848. doi: 10.11999/JET200110

Citation:

Quan YUAN, Wei YOU, Xinsheng JI, Hongbo TANG. Adaptive Scaling of Virtualized Network Function Resource Capacity[J]. Journal of Electronics & Information Technology, 2021, 43(7): 1841-1848. doi: 10.11999/JET200110

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Adaptive Scaling of Virtualized Network Function Resource Capacity

doi: 10.11999/JET200110

PLA Strategic Support Force Information Engineering University, Zhengzhou 450002, China

Funds: The National Natural Science Foundation of China (61801515), The National Natural Science Foundation Innovative Groups Project of China (61521003)

Received Date: 2020-02-17
Rev Recd Date: 2020-10-05

Available Online: 2020-12-14

Publish Date: 2021-07-10

Abstract

Abstract

In order to realize on-demand physical resource allocation in network function virtualization platform, an adaptive virtualized network function scaling method is proposed. The proposed method first use long short term memory network to realize traffic forecasting. Then combining with the forecasting result, a forward neural network-based approach is designed to predict resource demand of requested virtualized network function. Finally, according to the result of resource demand prediction, a dynamic encoding genetic algorithm is proposed to realize dynamic deployment of virtualized network function instances. The experiment results show that compared with existing scaling methods, the proposed scaling method can reduce the negative impact of inaccurate traffic forecasting, decrease the relative error of resource demand prediction as well as the total number of servers occupied by requested virtualized network function instances.

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References

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