Abstract
In 5G mobile network, SDN/NFV as a key technology is widely used in EPC networks. In order to cope with the increasing data service in the EPC of 5G network, we propose a proactive cache strategy based on the deep learning network SSAEs for content popularity prediction based on the SDN/NFV architecture, SNDLPC. Firstly, NFV/SDN technique is used to build a virtual distributed deep learning network SSAEs. Then, the SSAEs network parameters are unsupervised trained by the historical users’ data. Finally, the content popularity is predicted by SSAEs using the data of user request in whole network collected by SDN controller. The SDN controller generates the proactive caching strategy according to the prediction results and synchronizes it to each cache node through flowtable to implement the strategy. In the simulation, the SSAEs network structure parameters are compared and determined. Compared with other strategies, such as the typical Hash + LRU and Betw + LRU caching strategies, SVM prediction and the BPNN prediction algorithm, the proposed SNDLPC proactive cache strategy can significantly improve cache performance.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 61571141, Grant 61702120 and Grant 61672008, in part by Guangdong Science and technology development project under Grant 2017A090905023, in part by Guangdong Natural Science Foundation under Grant 2016A030311013, in part by the Excellent Young Teachers in Universities in Guangdong(Grant No. YQ2015105), in part by the Scientific and Technological Projects of Guangdong Province (2017A050501039), in part by Guangdong Provincial Application-oriented Technical Research and Development Special fund project (Grant 2016B010127006, Grant 2017B010125003).
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Lei, F., Dai, Q., Cai, J., Zhao, H., Liu, X., Liu, Y. (2018). A Proactive Caching Strategy Based on Deep Learning in EPC of 5G. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_72
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