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Making content caching policies 'smart' using the deepcache framework

Published: 28 January 2019 Publication History

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.

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Published In

cover image ACM SIGCOMM Computer Communication Review
ACM SIGCOMM Computer Communication Review  Volume 48, Issue 5
October 2018
83 pages
ISSN:0146-4833
DOI:10.1145/3310165
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 January 2019
Published in SIGCOMM-CCR Volume 48, Issue 5

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Author Tags

  1. DeepCache
  2. cache hit
  3. caching
  4. deep learning
  5. fake requests
  6. lstm
  7. machine learning
  8. popularity prediction
  9. prefetching
  10. proactive caching
  11. seq2seq
  12. smart caching policies
  13. video object caches

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  • (2023)Semantics-Enhanced Temporal Graph Networks for Content Caching and Energy SavingICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279564(1724-1729)Online publication date: 28-May-2023
  • (2023)Content-Driven Closeness Centrality Based Caching in Softwarized Edge NetworksICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10278928(3264-3269)Online publication date: 28-May-2023
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