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File Fragmentation in Mobile Devices: Measurement, Evaluation, and Treatment

Title
File Fragmentation in Mobile Devices: Measurement, Evaluation, and Treatment
Author(s)
Ji, ChengChang, Li-PinHahn, Sangwook ShaneLee, SungjinPan, RiweiShi, LiangKim, JihongXue, Chun Jason
DGIST Authors
Lee, Sungjin
Issued Date
2019-09
Type
Article
Article Type
Article
Author Keywords
Measurementsflash memoryfile fragmentationI/O performance
ISSN
1536-1233
Abstract
Mobile devices, such as smartphones, have become a necessity in our daily life. However, users may notice that after being used for a long time, mobile devices begin to experience sluggish response. Based on an empirical study on a set of aged mobile devices, we identified that file fragmentation is among the key factors that contribute to the progressive degradation of response time. This study takes a three-step approach: First, we designed a set of reproducible file-system aging processes based on User-Interface (UI) script replay. Through the aging processes, we confirmed that file fragmentation quickly emerged, and SQLite files were among the most severely fragmented files. Second, based on the workloads of a selection of popular mobile applications, we observed that file fragmentation did impact on user-perceived latencies. Specifically, the launching time of Chrome on an aged file system was 79\% slower than it was on a pristine file system. Third, we evaluated existing treatments of file fragmentation, including space preallocation, persistent journal space, and file defragmentation to understand their efficacies and limitations. We also evaluated a state-of-the-art copyless defragmenter, janusd, to show its advantage over the existing methods. IEEE
URI
http://hdl.handle.net/20.500.11750/9326
DOI
10.1109/TMC.2018.2869737
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • 이성진 Lee, Sungjin
  • Research Interests Computer System; System Software; Storage System; Non-volatile Memory; Flash-based SSD; Distributed Storage Systems
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Appears in Collections:
Department of Electrical Engineering and Computer Science Data-Intensive Computing Systems Laboratory 1. Journal Articles

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