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 networkspecific 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.

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Mobile applications often predict the future to make decisions in the present. Although such predictions are inherently uncertain, applications typically assume that they are completely accurate. This assumption can lead to incorrect decisions resulting in unnecessary delays, wasted resources, or worse. Instead, prediction error should be a fundamental consideration in mobile systems. Applications should consider uncertainty when weighing alternatives. When one alternative is not clearly superior to another, redundant strategies are often appropriate, resulting in much better performance at a very modest cost. To illustrate these ideas, we describe and implement several methods for quantifying uncertainty in mobile environments. Our system allows applications to explicitly weigh the tradeoff between the performance gained via redundancy and the cost of extra energy and cellular data resources spent, tailoring decisions to their relative importance. We adapt two systems to use this approach. Compared to both simple and adaptive strategies that do not reflect prediction error, our library improves application performance by up to a factor of two.

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