Adaptive Prefetching in Wireless Computing

In this paper, we consider a basic issue in wireless computing, where mobile devices (with relatively limited memory) fetch data (text, images, multimedia, etc.) from access points over wireless channels of fluctuating quality. When the channel quality is low, slow data downloads can contribute to application latency, and degradation of the user experience. To mitigate this issue, mobile devices can prefetch data during good quality channel periods preemptively, in anticipation of using them during low quality channel epochs. In other words, under favorable wireless channel conditions, the mobile terminal can prefetch data aggressively to reduce application latency when channel conditions have degraded. Considering channel fluctuations, memory constraints, and application latency, the issue at each point in time is whether to prefetch or not. A dynamic programming approach is taken here to determine the optimal prefetching policy. The latter is leveraged to develop a prefetching algorithm “Fetch-or-Not” (FON). We then design a randomized “Fetch-or-Not” (RFON) prefetching algorithm, which uses a randomized approximation of FON, thus lifting the need for online optimization and substantially reducing the computational complexity. Simulations are used to demonstrate that our low-complexity schemes perform well when compared to the optimal. In addition, our schemes outperform benchmark design techniques in realistic channel conditions.