DEEPCACHE: A deep learning based framework for content caching

Arvind Narayanan, Saurabh Verma, Eman Ramadan, Pariya Babaie, Zhi-Li Zhang

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

42 Scopus citations

Abstract

In this paper, we present DEEPCACHE a novel Framework for content caching, which can significantly boost cache performance. Our Framework is based on powerful deep recurrent neural network models. It comprises of two main components: i) Object Characteristics Predictor, which builds upon deep LSTM Encoder-Decoder model to predict the future characteristics of an object (such as object popularity) - to the best of our knowledge, we are the first to propose LSTM Encoder-Decoder model for content caching; ii) a caching policy component, which accounts for predicted information of objects to make smart caching decisions. In our thorough experiments, we show that applying DEEPCACHE Framework to existing cache policies, such as LRU and k-LRU, significantly boosts the number of cache hits.

Original languageEnglish (US)
Title of host publicationNetAI 2018 - Proceedings of the 2018 Workshop on Network Meets AI and ML, Part of SIGCOMM 2018
PublisherAssociation for Computing Machinery, Inc
Pages48-53
Number of pages6
ISBN (Electronic)9781450359115
DOIs
StatePublished - Aug 7 2018
Event2018 ACM SIGCOMM Workshop on Network Meets AI and ML, NetAI 2018 - Budapest, Hungary
Duration: Aug 24 2018 → …

Publication series

NameNetAI 2018 - Proceedings of the 2018 Workshop on Network Meets AI and ML, Part of SIGCOMM 2018

Other

Other2018 ACM SIGCOMM Workshop on Network Meets AI and ML, NetAI 2018
Country/TerritoryHungary
CityBudapest
Period8/24/18 → …

Keywords

  • Cache hit
  • Caching
  • Deep learning
  • DeepCache
  • Fake requests
  • Lstm
  • Machine learning
  • Popularity prediction
  • Prefetching
  • Proactive caching
  • Seq2seq
  • Smart caching policies
  • Video object caches

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