Cocktail edge caching: Ride dynamic trends of content popularity with ensemble learning

Tongyu Zong, Chen Li, Yuanyuan Lei, Guangyu Li, Houwei Cao, Yong Liu

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

Abstract

Edge caching will play a critical role in facilitating the emerging content-rich applications. However, it faces many new challenges, in particular, the highly dynamic content popularity and the heterogeneous caching configurations. In this paper, we propose Cocktail Edge Caching, that tackles the dynamic popularity and heterogeneity through ensemble learning. Instead of trying to find a single dominating caching policy for all the caching scenarios, we employ an ensemble of constituent caching policies and adaptively select the best-performing policy to control the cache. Towards this goal, we first show through formal analysis and experiments that different variations of the LFU and LRU polices have complementary performance in different caching scenarios. We further develop a novel caching algorithm that enhances LFU/LRU with deep recurrent neural network (LSTM) based time-series analysis. Finally, we develop a deep reinforcement learning agent that adaptively combines base caching policies according to their virtual hit ratios on parallel virtual caches. Through extensive experiments driven by real content requests from two large video streaming platforms, we demonstrate that CEC not only consistently outperforms all single policies, but also improves the robustness of them. CEC can be well generalized to different caching scenarios with low computation overheads for deployment.

Original languageEnglish (US)
Title of host publicationINFOCOM 2021 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738112817
DOIs
StatePublished - May 10 2021
Event40th IEEE Conference on Computer Communications, INFOCOM 2021 - Vancouver, Canada
Duration: May 10 2021May 13 2021

Publication series

NameProceedings - IEEE INFOCOM
Volume2021-May
ISSN (Print)0743-166X

Conference

Conference40th IEEE Conference on Computer Communications, INFOCOM 2021
Country/TerritoryCanada
CityVancouver
Period5/10/215/13/21

Keywords

  • Deep reinforcement learning
  • Edge caching
  • LSTM
  • Video

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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