Informed Mobile Prefetching

Brett D. Higgins, Jason Flinn, T.J. Giuli, Brian Noble, Christopher Peplin, and David Watson

Abstract

Prefetching is a double-edged sword. It can hide the latency of data transfers over poor and intermittently connected wireless networks, but the costs of prefetching in terms of increased energy and cellular data usage are potentially substantial, particularly for data prefetched incorrectly. Weighing the costs and benefits of prefetching is complex, and consequently most mobile applications employ simple but sub-optimal strategies.

Rather than leave the job to applications, we argue that the underlying mobile system should provide explicit prefetching support. Our prototype, IMP, presents a simple interface that hides the complexity of the prefetching decision. IMP uses a cost-benefit analysis to decide when to prefetch data. It employs goal-directed adaptation to try to minimize application response time while meeting budgets for battery lifetime and cellular data usage. IMP opportunistically uses available networks while ensuring that prefetches do not degrade network performance for foreground activity. It tracks hit rates for past prefetches and accounts for network-specific costs in order to dynamically adapt its prefetching strategy to both the network conditions and the accuracy of application prefetch disclosures. Experiments with email and news reader applications show that IMP provides predictable usage of budgeted resources, while lowering application response time compared to the oblivious strategies used by current applications.