Liang, YuYuLiangPan, RiweiRiweiPanRen, TianyuTianyuRenCui, YufeiYufeiCuiAusavarungnirun, RachataRachataAusavarungnirunChen, XianzhangXianzhangChenLi, ChanglongChanglongLiTEI-WEI KUOXue, Chun JasonChun JasonXue2023-07-172023-07-172022-01-019781939133267https://scholars.lib.ntu.edu.tw/handle/123456789/633672Mobile applications often maintain downloaded data as cache files in local storage for a better user experience. These cache files occupy a large portion of writes to mobile flash storage and have a significant impact on the performance and lifetime of mobile devices. Different from current practice, this paper proposes a novel framework, named CacheSifter, to differentiate cache files and treat cache files based on their reuse behaviors and main-memory/storage usages. Specifically, CacheSifter classifies cache files into three categories online and greatly reduces the number of writebacks on flash by dropping cache files that most likely will not be reused. We implement CacheSifter on real Android devices and evaluate it over representative applications. Experimental results show that CacheSifter reduces the writebacks of cache files by an average of 62% and 59.5% depending on the ML models, and the I/O intensive write performance of mobile devices could be improved by an average of 18.4% and 25.5%, compared to treating cache files equally.conference paper2-s2.0-85140386798https://api.elsevier.com/content/abstract/scopus_id/85140386798