Feedforward Neural Networks for Caching: Enough or Too Much?

Vladyslav Fedchenko; Giovanni Neglia; Bruno Ribeiro

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arXiv · 1810.06930

Feedforward Neural Networks for Caching: Enough or Too Much?

Vladyslav Fedchenko·Giovanni Neglia·Bruno Ribeiro

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Abstract

We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a new policy relying on the more complex recurrent neural networks. At the same time, replacing the FNN predictor with a naive linear estimator does not degrade caching performance significantly, questioning then the role of neural networks for these applications.

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BibTeX RIS

Vladyslav Fedchenko, Giovanni Neglia, Bruno Ribeiro. 2018-10-16. Feedforward Neural Networks for Caching: Enough or Too Much?. https://arxiv.org/abs/1810.06930

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