Intelligent mobile edge network caching based on deep learning

doi:10.7523/j.issn.2095-6134.2020.01.015

Abstract

Abstract: In view of mobile edge network caching problem, computation resources are further pushed to the network edge to enable data analysis and to build deep learning-based caching strategy at access points, thereby boosting caching gain. The long short term memory (LSTM)-based neural network is proposed to predict the future content popularity by analysing the local data, which is further used to optimize content replacement for the cache hit rate maximization and construct deep caching strategy. Real-world dataset is used to validate the effectiveness of the proposed deep caching strategy. Numerical results demonstrate that our content popularity prediction method outperforms the state-of-art prediction method. Compared with traditional methods, the caching system needs only approximately half storage space to achieve the same cache hit rate.

Key words: mobile edge network caching, deep learning, long short term memory(LSTM) neural network, file popularity prediction, cache hit rate

CLC Number:

TN92

SONG Xuming, SHEN Yifei, SHI Yuanming. Intelligent mobile edge network caching based on deep learning[J]. , 2020, 37(1): 128-135.

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