A deep reinforcement learning-based framework for content caching

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Scopus citations

Abstract

Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, this work presents a DRL-based framework with Wolpertinger architecture for content caching at the base station. The proposed framework is aimed at maximizing the long-term cache hit rate, and it requires no knowledge of the content popularity distribution. To evaluate the proposed framework, we compare the performance with other caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU), and First-In First-Out (FIFO) caching strategies. Meanwhile, since the Wolpertinger architecture can effectively limit the action space size, we also compare the performance with Deep Q-Network to identify the impact of dropping a portion of the actions. Our results show that the proposed framework can achieve improved short-term cache hit rate and improved and stable long-term cache hit rate in comparison with LRU, LFU, and FIFO schemes. Additionally, the performance is shown to be competitive in comparison to Deep Q-learning, while the proposed framework can provide significant savings in runtime.

Original languageEnglish (US)
Title of host publication2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538605790
DOIs
StatePublished - May 21 2018
Event52nd Annual Conference on Information Sciences and Systems, CISS 2018 - Princeton, United States
Duration: Mar 21 2018Mar 23 2018

Other

Other52nd Annual Conference on Information Sciences and Systems, CISS 2018
CountryUnited States
CityPrinceton
Period3/21/183/23/18

Keywords

  • content caching
  • Deep reinforcement learning
  • Wolpertinger architecture

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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    Zhong, C., Gursoy, M. C., & Velipasalar, S. (2018). A deep reinforcement learning-based framework for content caching. In 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018 (pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2018.8362276