MUSTACHE: Multi-Step-Ahead Predictions for Cache Eviction
Gabriele Tolomei, Lorenzo Takanen, Fabio Pinelli

TL;DR
MUSTACHE is a novel cache replacement algorithm that uses deep learning to predict future page requests, leading to improved cache hit ratios and reduced read/write operations compared to traditional heuristics.
Contribution
It introduces a deep learning-based multi-step-ahead prediction approach for cache eviction, integrating forecasting into cache management for the first time.
Findings
Outperforms exact LRU in cache hit ratio by 1.9%.
Reduces cache misses' read/write operations by over 10%.
Demonstrates effectiveness on benchmark datasets.
Abstract
In this work, we propose MUSTACHE, a new page cache replacement algorithm whose logic is learned from observed memory access requests rather than fixed like existing policies. We formulate the page request prediction problem as a categorical time series forecasting task. Then, our method queries the learned page request forecaster to obtain the next predicted page memory references to better approximate the optimal B\'el\'ady's replacement algorithm. We implement several forecasting techniques using advanced deep learning architectures and integrate the best-performing one into an existing open-source cache simulator. Experiments run on benchmark datasets show that MUSTACHE outperforms the best page replacement heuristic (i.e., exact LRU), improving the cache hit ratio by 1.9% and reducing the number of reads/writes required to handle cache misses by 18.4% and 10.3%.
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Taxonomy
TopicsCaching and Content Delivery · Advanced Data Storage Technologies · Recommender Systems and Techniques
