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Overcoming the inherent challenges. 

Spatiotemporal variation in cellular bandwidth availability is well-known and could affect a mobile user's quality of experience (QoE), especially while using bandwidth intensive streaming applications such as movies, podcasts, and music videos during commute. If such variations are made available to a streaming service in advance it could perhaps plan better to avoid sub-optimal performance while the user travels through regions of low bandwidth availability. The intuition is that such future knowledge could be used to buffer additional content in regions of higher bandwidth availability to tide over the deficits in regions of low bandwidth availability. Foresight is a service designed to provide this future knowledge for client apps running on a mobile device. It comprises three components: (a) a crowd-sourced bandwidth estimate reporting facility, (b) an on-cloud bandwidth service that records the spatiotemporal variations in bandwidth and serves queries for bandwidth availability from mobile users, and (c) an on-device bandwidth manager that caters to the bandwidth requirements from client apps by providing them with bandwidth allocation schedules. Foresight is implemented in the Android framework. As a proof of concept for using this service, we have modified an open-source video player---Exoplayer---to use the results of Foresight in its video buffer management. Our performance evaluation shows Foresight's scalability. We also showcase the opportunity that Foresight offers to ExoPlayer to enhance video quality of experience (QoE) despite spatiotemporal bandwidth variations for metrics such as overall higher bitrate of playback, reduction in number of bitrate switches, and reduction in the number of stalls during video playback. 

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.