TY - JOUR
T1 - Where did my 256 GB go≥ A Measurement Analysis of Storage Consumption on Smart Mobile Devices
AU - Bijlani, Ashish
AU - Ramachandran, Umakishore
AU - Campbell, Roy
N1 - Funding Information:
We would like to thank our shepherd, Dr. Arif Merchant, and all anonymous reviewers for their insightful feedback and suggestions, which substantially improved the content and presentation of this paper. This work was funded in part by NSF CPS program Award #1446801, NSF CNS program Award #1909346, and a gift from Microsoft Corp.
Publisher Copyright:
© 2021 ACM.
PY - 2021/6
Y1 - 2021/6
N2 - This work presents the first-ever detailed and large-scale measurement analysis of storage consumption behavior of applications (apps) on smart mobile devices. We start by carrying out a five-year longitudinal static analysis of millions of Android apps to study the increase in their sizes over time and identify various sources of app storage consumption. Our study reveals that mobile apps have evolved as large monolithic packages that are packed with features to monetize/engage users and optimized for performance at the cost of redundant storage consumption. We also carry out a mobile storage usage study with 140 Android participants. We built and deployed a lightweight context-aware storage tracing tool, called cosmos, on each participant's device. Leveraging the traces from our user study, we show that only a small fraction of apps/features are actively used and usage is correlated to user context. Our findings suggest a high degree of app feature bloat and unused functionality, which leads to inefficient use of storage. Furthermore, we found that apps are not constrained by storage quota limits, and developers freely abuse persistent storage by frequently caching data, creating debug logs, user analytics, and downloading advertisements as needed. Finally, drawing upon our findings, we discuss the need for efficient mobile storage management, and propose an elastic storage design to reclaim storage space when unused. We further identify research challenges and quantify expected storage savings from such a design. We believe our findings will be valuable to the storage research community as well as mobile app developers.
AB - This work presents the first-ever detailed and large-scale measurement analysis of storage consumption behavior of applications (apps) on smart mobile devices. We start by carrying out a five-year longitudinal static analysis of millions of Android apps to study the increase in their sizes over time and identify various sources of app storage consumption. Our study reveals that mobile apps have evolved as large monolithic packages that are packed with features to monetize/engage users and optimized for performance at the cost of redundant storage consumption. We also carry out a mobile storage usage study with 140 Android participants. We built and deployed a lightweight context-aware storage tracing tool, called cosmos, on each participant's device. Leveraging the traces from our user study, we show that only a small fraction of apps/features are actively used and usage is correlated to user context. Our findings suggest a high degree of app feature bloat and unused functionality, which leads to inefficient use of storage. Furthermore, we found that apps are not constrained by storage quota limits, and developers freely abuse persistent storage by frequently caching data, creating debug logs, user analytics, and downloading advertisements as needed. Finally, drawing upon our findings, we discuss the need for efficient mobile storage management, and propose an elastic storage design to reclaim storage space when unused. We further identify research challenges and quantify expected storage savings from such a design. We believe our findings will be valuable to the storage research community as well as mobile app developers.
KW - mobile
KW - smartphones
KW - storage management
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U2 - 10.1145/3460095
DO - 10.1145/3460095
M3 - Article
AN - SCOPUS:85107903825
VL - 5
JO - Proceedings of the ACM on Measurement and Analysis of Computing Systems
JF - Proceedings of the ACM on Measurement and Analysis of Computing Systems
SN - 2476-1249
IS - 2
M1 - 28
ER -