Hierarchical caching via deep reinforcement learning

Alireza Sadeghi, Gang Wang, Georgios B. Giannakis

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

Abstract

Wireless and wireline networks, such as Internet, cellular, and content delivery networks are to serve end-user file requests proactively. To this aim, by storing anticipated highly popular files during off-peak periods, and fetching them to end-users during on-peak instances, these networks smoothen out the load fluctuations on the back-haul links. In this context, several practical networks comprise a parent caching node connected to multiple leaf nodes to serve end-user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning formulation is put forth in this work. Furthermore, to endow with scalability so that the algorithm can effectively handle the curse of dimensionality, a deep reinforcement learning approach is also developed. Our novel caching policy relies on a deep Q-network to enforce the parent node with ability to learn-and-adapt to unknown policies of leaf nodes as well as spatio-temporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3532-3536
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

Keywords

  • Content delivery
  • Reinforcement learning

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