Video Request Prediction and Cooperative Caching Strategy Based on Federated Learning in Mobile Edge Computing

LI Guanghui; LI Yijing; HU Shihong

doi:10.11999/JEIT211287

Volume 45 Issue 1

Jan. 2023

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LI Guanghui, LI Yijing, HU Shihong. Video Request Prediction and Cooperative Caching Strategy Based on Federated Learning in Mobile Edge Computing[J]. Journal of Electronics & Information Technology, 2023, 45(1): 218-226. doi: 10.11999/JEIT211287

Citation:

LI Guanghui, LI Yijing, HU Shihong. Video Request Prediction and Cooperative Caching Strategy Based on Federated Learning in Mobile Edge Computing[J]. Journal of Electronics & Information Technology, 2023, 45(1): 218-226. doi: 10.11999/JEIT211287

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Video Request Prediction and Cooperative Caching Strategy Based on Federated Learning in Mobile Edge Computing

doi: 10.11999/JEIT211287

School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China

Funds: The National Natural Science Foundation of China (62072216)

Received Date: 2021-11-17
Accepted Date: 2022-03-03
Rev Recd Date: 2022-02-22

Available Online: 2022-03-08

Publish Date: 2023-01-17

Abstract

Abstract

With the rise of Internet social platforms and the popularization of mobile smart terminal devices, people's demand for high-quality and real-time data has risen sharply, especially for video services such as short videos and live streams. At the same time, too many terminal devices connected to the core network increase the load of the backhaul link, so that the traditional cloud computing is difficult to meet the low-latency requirements of users for video services. By deploying edge nodes with computing and storage capabilities at the edge of the network, Mobile Edge Computing (MEC) can calculate and store closer to the users, which will reduce the data transmission delay and alleviate the network congestion. Therefore, making full use of the computing and storage resources at the edge of the network under MEC, a video request prediction method and a cooperative caching strategy based on federated learning are proposed. By federally training the proposed model Deep Request Prediction Network (DRPN) with multiple edge nodes, the video requests in the future can be predicted and then cache decisions can be made cooperatively. The simulation results show that compared with other strategies, the proposed strategy can not only effectively improve the cache hit rate and reduce the user waiting delay, but also reduce the communication cost and cache cost of the whole system to a certain extent.
- Mobile Edge Computing(MEC),
- Federated learning,
- Video caching,
- Request prediction

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References(16)

References

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