@inproceedings{bd57ec3089074682a2167476ced206d0,
title = "SS-LRU: A Smart Segmented LRU Caching",
abstract = "Many caching policies use machine learning to predict data reuse, but they ignore the impact of incorrect prediction on cache performance, especially for large-size objects. In this paper, we propose a smart segmented LRU (SS-LRU) replacement policy, which adopts a size-aware classifier designed for cache scenarios and considers the cache cost caused by misprediction. Besides, SS-LRU enhances the migration rules of segmented LRU (SLRU) and implements a smart caching with unequal priorities and segment sizes based on prediction and multiple access patterns. We conducted Extensive experiments under the real-world workloads to demonstrate the superiority of our approach over state-of-the-art caching policies.",
keywords = "cache replacement, cost-sensitive, machine learning, smart",
author = "Chunhua Li and Man Wu and Yuhan Liu and Ke Zhou and Ji Zhang and Yunqing Sun",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 59th ACM/IEEE Design Automation Conference, DAC 2022 ; Conference date: 10-07-2022 Through 14-07-2022",
year = "2022",
month = jul,
day = "10",
doi = "10.1145/3489517.3530469",
language = "English",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "397--402",
booktitle = "Proceedings of the 59th ACM/IEEE Design Automation Conference, DAC 2022",
address = "United States",
}