Data deduplication technology has gained popularity in modern file systems due to its ability to eliminate redundant writes and improve storage space efficiency. In recent years, the flash-friendly file system (F2FS) has been widely adopted in flash memory-based storage devices, including smartphones, fast-speed servers, and Internet of Things. In this article, we propose F2DFS (deduplication-based F2FS), which introduces three main design contributions. First, F2DFS integrates inline and offline hybrid deduplication. Inline deduplication eliminates redundant writes and enhances flash device endurance, while offline deduplication mitigates the negative I/O performance impact and saves more storage space. Second, F2DFS follows the file system coupling design principle, effectively leveraging the potentials and benefits of both deduplication and native F2FS. Also, with the aid of this principle, F2DFS achieves high-performance and space-efficient incremental deduplication. Third, F2DFS adopts virtual indexing to mitigate deduplication-induced many-to-one mapping updates during the segment cleaning. We conducted comprehensive experimental comparisons between F2DFS, native F2FS, and other state-of-the-art deduplication schemes, using both synthetic and real-world workloads. For inline deduplication, F2DFS outperforms SmartDedup, Dmdedup, and ZFS, in terms of both I/O bandwidth performance and deduplication rates. And for offline deduplication, compared to SmartDedup, XFS, and BtrFS, F2DFS shows higher execution efficiency, lower resource usage, and greater storage space savings. Moreover, F2DFS demonstrates more efficient segment cleanings than native F2FS.

Multipath transport allows the simultaneous use of diverse paths on mobile devices to maximize mobile resource usage. Over the years, we have witnessed several mobile multipath deployment examples by network operators and mobile app providers. However, existing deployment methods require modifications to either the network infrastructure or both endpoints. To lower the bar of the deployment, we present Fleety, a mobile system service that provides the multi-path transport capability with client-only modification. To the best of our knowledge, we are the first to carry out a large-scale mobile multipath deployment that can support hundreds of mobile applications in the cross-ISP setting. This paper is a retrospective of our experience in building and deploying multipath transport for mobile applications. We reveal several practical deployment challenges and share our experience in dealing with them.

Energy consumption is one of the most important aspects of mobile apps. During energy testing, it is important for developers to understand not only the energy consumption rate of an app, but also why energy is consumed. However, existing energy testing tools are more concerned about the accuracy of energy estimation, while typically not providing explanations on why and how exactly energy has been consumed. This paper presents E-Spector, an online energy inspection method for Android apps, which can not only visualize the energy consumption of an app in an instant online manner, but also can tell what happened behind each energy hotspot on the energy curve. E-Spector relies on static analysis and app instrumentation to collect the activities from an app execution in real-time. Then it presents the activities on an instant energy curve, such that the user can easily tell what happened behind each energy spike. Experimental result shows that the energy estimation error of E-Spector is less than 10% and its overhead on energy consumption is about 4%. We also show case studies to demonstrate the applicability and effectiveness of E-Spector in energy monitoring, analysis and bug inspection.

It becomes very important to understand power characteristics of mobile applications because more and more complex applications are running on modern smartphones. Although many techniques have been proposed to estimate the power dissipation rate for mobile applications, it typically requires hardware support (i.e., power meters) or complex power models (software profiling or hardware parameters). These techniques might work well in labs with a small set of applications. However, it becomes impractical when we try to estimate the power of mobile applications in an uncontrolled environment. This paper proposes a novel method for estimating the power consumption of mobile applications with profile-based battery traces. Battery traces can be easily collected through a user-level application on any devices. Although it is difficult to achieve accurate results for only a few users because battery changes are coarse-grained, the method is expected to reach an accurate estimation when the number of battery traces reaches a certain scale. Our experiments based on battery traces from more than 80,000 users demonstrate that it is possible to estimate application power with only coarse-grained battery traces. The results are also validated with measured power numbers from a Monsoon power monitor.