学习前向重用距离
Learning Forward Reuse Distance
摘要：
缓存技术在云计算时代从应用到基础设施、计算机体系结构中的Web缓存、Memcached和内存缓存等被广泛使用。对缓存数据的预测可以极大地帮助提高缓存管理和性能。深度学习技术的最新进展使得能够设计新颖的智能缓存替换策略。在这项工作中，我们提出了一个学习辅助的方法来预测未来的数据访问。我们发现，一个强大的基于LSTM的递归神经网络模型可以提供高的预测精度的基础上，只有一个缓存跟踪作为输入。高精度来自精心制作的本地驱动功能设计。受高预测精度的启发，我们提出了一个伪OPT策略，并在Microsoft Research的13个实际存储工作负载上进行了评估。结果表明，新的缓存策略提高了最高达19.2%的最先进的实际政策，并招致平均只有2.3%的高失误率比OPT。
英文摘要：
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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