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 language | English (US) |
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Title of host publication | NetAI 2018 - Proceedings of the 2018 Workshop on Network Meets AI and ML, Part of SIGCOMM 2018 |
Publisher | Association for Computing Machinery, Inc |
Pages | 48-53 |
Number of pages | 6 |
ISBN (Electronic) | 9781450359115 |
DOIs | |
State | Published - Aug 7 2018 |
Event | 2018 ACM SIGCOMM Workshop on Network Meets AI and ML, NetAI 2018 - Budapest, Hungary Duration: Aug 24 2018 → … |
Publication series
Name | NetAI 2018 - Proceedings of the 2018 Workshop on Network Meets AI and ML, Part of SIGCOMM 2018 |
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Other
Other | 2018 ACM SIGCOMM Workshop on Network Meets AI and ML, NetAI 2018 |
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Country/Territory | Hungary |
City | Budapest |
Period | 8/24/18 → … |
Bibliographical note
Publisher Copyright:© 2018 Association for Computing Machinery.
Keywords
- Cache hit
- Caching
- Deep learning
- DeepCache
- Fake requests
- Lstm
- Machine learning
- Popularity prediction
- Prefetching
- Proactive caching
- Seq2seq
- Smart caching policies
- Video object caches