2022/11/03 by Gabriele Tolomei, Tolomei, Gabriele, Lorenzo Takanen +3
Computer Science · #Advanced Data Storage Technologies #Caching and Content Delivery #FOS: Computer and information sciences #Machine Learning (cs.LG) #Operating Systems (cs.OS) #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.2211.02177
openalex publication_date 2022/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 k predicted page memory references to better approximate the optimal Bélády'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%.