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When mobile apps are used extensively in our daily lives, their responsiveness has become an important factor that can negatively impact the user experience. The long response time of a mobile app can be caused by a variety of reasons, including soft hang bugs or prolonged user interface APIs (UI-APIs). While hang bugs have been researched extensively before, our investigation on UI-APIs in today’s mobile OS finds that the recursive construction of UI view hierarchy often can be time-consuming, due to the complexity of today’s UI views. To accelerate UI processing, such complex views can be pre-processed and cached before the user even visits them. However, pre-caching every view in a mobile app is infeasible due to the incurred overheads on time, energy, and cache space. In this paper, we propose MAPP, a framework for Mobile App Predictive Pre-caching. MAPP has two main modules, 1) UI view prediction based on deep learning and 2) UI-API pre-caching, which coordinate to improve the responsiveness of mobile apps. MAPP adopts a per-user and per-app prediction model that is tailored based on the analysis of collected user traces, such as location, time, or the sequence of previously visited views. A dynamic feature ranking and model selection algorithm is designed to judiciously filter out less relevant features for improving the prediction accuracy with less computation overhead. MAPP is evaluated with 61 real-world traces from 18 volunteers over 30 days to show that it can shorten the response time of mobile apps by 59.84% on average with an average cache hit rate of 92.55%.
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Marauder: Synergized Caching and Prefetching for Low-Risk Mobile App Acceleration
Low interaction response times are crucial to the experience that mobile apps provide for their users. Unfortunately, existing strategies to alleviate the network latencies that hinder app responsiveness fall short in practice. In particular, caching is plagued by challenges in setting expiration times that match when a resource's content changes, while prefetching hinges on accurate predictions of user behavior that have proven elusive. We present Marauder, a system that synergizes caching and prefetching to improve the speedups achieved by each technique while avoiding their inherent limitations. Key to Marauder is our observation that, like web pages, apps handle interactions by downloading and parsing structured text resources that entirely list (i.e., without needing to consult app binaries) the set of other resources to load. Building on this, Marauder introduces two low-risk optimizations directly from the app's cache. First, guided by cached text files, Marauder prefetches referenced resources during an already-triggered interaction. Second, to improve the efficacy of cached content, Marauder judiciously prefetches about-to-expire resources, extending cache lives for unchanged resources, and downloading updates for lightweight (but crucial) text files. Across a wide range of apps, live networks, interaction traces, and phones, Marauder reduces median and 90th percentile interaction response times by 27.4% and 43.5%, while increasing data usage by only 18%.
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- PAR ID:
- 10323096
- Date Published:
- Journal Name:
- 19th Annual International Conference on Mobile Systems, Applications, and Services
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3458864.3466866
