A Smart Cache Content Update Policy Based on Deep Reinforcement Learning
Joint Authors
Li, Lincan
Kwong, Chiew Foong
Liu, Qianyu
Wang, Jing
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-09
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency.
In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location.
Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP).
Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem.
Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory.
The DQN agent derives the optimal policy for the cache decision.
Compared with the existing policies, the simulation results show that our proposed policy is 56%–64% improved in terms of the cache hit ratio and 56%–59% decreased in terms of the average latency.
American Psychological Association (APA)
Li, Lincan& Kwong, Chiew Foong& Liu, Qianyu& Wang, Jing. 2020. A Smart Cache Content Update Policy Based on Deep Reinforcement Learning. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1214660
Modern Language Association (MLA)
Li, Lincan…[et al.]. A Smart Cache Content Update Policy Based on Deep Reinforcement Learning. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1214660
American Medical Association (AMA)
Li, Lincan& Kwong, Chiew Foong& Liu, Qianyu& Wang, Jing. A Smart Cache Content Update Policy Based on Deep Reinforcement Learning. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1214660
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214660