Mobile phones have evolved from communication devices to indispensable accessories with access to real-time content. The increasing reliance on dynamic content comes at the cost of increased latency to pull the content from the Internet be- fore the user can start using it. While prior work has ex- plored parts of this problem, they ignore the bandwidth costs of prefetching, incur significant training overhead, need sev- eral sensors to be turned on, and do not consider practical sys- tems issues that arise from the limited background processing capability supported by mobile operating systems. In this pa- per, we make app prefetch practical on mobile phones. Our contributions are two-fold. First, we design an app predic- tion algorithm, APPM, that requires no prior training, adapts to usage dynamics, predicts not only which app will be used next but also when it will be used, and provides high accu- racy without requiring additional sensor context. Second, we perform parallel prefetch on screen unlock, a mechanism that leverages the benefits of prediction while operating within the constraints of mobile operating systems. Our experiments are conducted on long-term traces, live deployments on the An- droid Play Market, and user studies, and show that we out- perform prior approaches to predicting app usage, while also providing practical ways to prefetch application content on mobile phones.
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