Learning Forward Reuse Distance

• URL: http://arxiv.org/abs/2007.15859v1
• Date: Fri, 31 Jul 2020 05:57:50 GMT
• Title: Learning Forward Reuse Distance
• Authors: Pengcheng Li, Yongbin Gu
• Abstract summary: Recent advancement of deep learning techniques enables the design of novel intelligent cache replacement policies. We find that a powerful LSTM-based recurrent neural network model can provide high prediction accuracy based on only a cache trace as input. Results demonstrate that the new cache policy improves state-of-art practical policies by up to 19.2% and incurs only 2.3% higher miss ratio than OPT on average.
• Score: 1.8777512961936749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Caching techniques are widely used in the era of cloud computing from applications, such as Web caches to infrastructures, Memcached and memory caches in computer architectures. Prediction of cached data can greatly help improve cache management and performance. The recent advancement of deep learning techniques enables the design of novel intelligent cache replacement policies. In this work, we propose a learning-aided approach to predict future data accesses. We find that a powerful LSTM-based recurrent neural network model can provide high prediction accuracy based on only a cache trace as input. The high accuracy results from a carefully crafted locality-driven feature design. Inspired by the high prediction accuracy, we propose a pseudo OPT policy and evaluate it upon 13 real-world storage workloads from Microsoft Research. Results demonstrate that the new cache policy improves state-of-art practical policies by up to 19.2% and incurs only 2.3% higher miss ratio than OPT on average.

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