1. Information about the paper


Parate, Abhinav, Matthias Böhmer, David Chu, Deepak Ganesan, and Benjamin M. Marlin. “Practical prediction and prefetch for faster access to applications on mobile phones.” In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp. 275-284. ACM, 2013.


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 before the user can start using it. While prior work has explored parts of this problem, they ignore the bandwidth costs of prefetching, incur significant training overhead, need several sensors to be turned on, and do not consider practical systems issues that arise from the limited background processing capability supported by mobile operating systems. In this paper, we make app prefetch practical on mobile phones. Our contributions are two-fold. First, we design an app prediction 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 accuracy 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 Android Play Market, and user studies, and show that we outperform prior approaches to predicting app usage, while also providing practical ways to prefetch application content on mobile phones.


2. My review of the paper

  1. This paper successfully showed that in prefetching apps, we not only need to predict WHAT app is going to be used next, we also need to predict WHEN an app is going to be launched. This additional context was shown that it is useful to lower bandwith cost and thus energy cost accordingly, and also gives fresher contents to users.
  2. Prefetching based on time only can incur more data cost when it is done on cellular network. Prefetching based on WiFi availability will lower cellular data cost but may lower contents freshness. There are always trade-offs in every scenario.
  3. For better trade-off, I propose that we put weight on apps so that an app can get more weight on the freshness or on the WiFi availability, depending on users need and preference.