CacheSifter: Sifting Cache Files for Boosted Mobile Performance and Lifetime

Authors: 

Yu Liang, Department of Computer Science, City University of Hong Kong and School of Cyber Science and Technology, Zhejiang University; Riwei Pan, Tianyu Ren, and Yufei Cui, Department of Computer Science, City University of Hong Kong; Rachata Ausavarungnirun, TGGS, King Mongkut's University of Technology North Bangkok; Xianzhang Chen, College of Computer Science, Chongqing University; Changlong Li, School of Computer Science and Technology, East China Normal University; Tei-Wei Kuo, Department of Computer Science, City University of Hong Kong, Department of Computer Science and Information Engineering, National Taiwan University, and NTU High Performance and Scientific Computing Center, National Taiwan University; Chun Jason Xue, Department of Computer Science, City University of Hong Kong

Abstract: 

Mobile 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.

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BibTeX
@inproceedings {277842,
title = {{CacheSifter}: Sifting Cache Files for Boosted Mobile Performance and Lifetime},
booktitle = {20th USENIX Conference on File and Storage Technologies (FAST 22)},
year = {2022},
isbn = {978-1-939133-26-7},
address = {Santa Clara, CA},
pages = {445-459},
url = {https://www.usenix.org/conference/fast22/presentation/liang},
publisher = {USENIX Association},
month = feb,
}

Presentation Video