Making content caching policies 'Smart' using the DEEPCACHE framework

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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, signiicantly boosts the number of cache hits.

Original languageEnglish (US)
Pages (from-to)64-69
Number of pages6
JournalComputer Communication Review
Issue number5
StatePublished - Oct 2018


  • 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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