EICache: A learning-based intelligent caching strategy in mobile edge computing

doi:10.1007/s12083-021-01266-4

2021-11-12

PRE

Bing Tang *

Linyao Kang

DOI：10.1007/s12083-021-01266-4

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With the rapid development of 5G mobile networks, data traffic has increased dramatically and is putting tremendous pressure on the backhaul link. In 5G-based mobile edge computing (MEC) environment, efficient caching at the edge of the network provides a solution for satisfying the quality of experience (QoE) requirements for lower latency. An intelligent caching strategy for MEC based on machine learning has been proposed, namely EICache, which considers the user's mobility and interest preferences. It could predict user's mobility using historical trajectory based on Long Short-Term Memory (LSTM) algorithm, and predict interest using Gradient Boosting Decision Tree (GBDT) method, to obtain the content of interest in advance, and then cache the content in advance on the neighboring edge node where the user is likely to go. Performance evaluations have been conducted using public YouTube trending video datasets from Kaggle and real trajectory datasets, compared with different cache replacement methods. The metrics of the cache hit rate, and the overall request latency are used for evaluation. By training the datasets first and then predicting, the accuracy of LSTM-based location prediction is about 80%, and the accuracy of GBDT-based interest prediction reaches about 25.4%. The hit rate of the edge caching strategy is increased by 40.5% compared with the strategy of random caching without any predictions. The results have proved the efficiency of EICache, which could meet the user's QoE requirements of low request latency.

Keyword:

Mobile edge computing

Edge caching

Machine learning

Mobility prediction

Interest prediction

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Peer-to-Peer Networking and Applications

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2.6

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2.2K

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2.9K

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