  The processing, computation and memory requirements posed by emerging mobile
broadband services require adaptive memory management and prefetching
techniques at the mobile terminals for satisfactory application performance and
sustained device battery lifetime. In this work we investigate a scenario where
tasks with varied computational requirements are fetched by a mobile device
from a central server over an error prone wireless link. We examine the buffer
dynamics at the mobile terminal and the central server under varying wireless
channel connectivity and device memory congestion states as variable sizes
tasks are executed on the terminal. Our goal is to minimize the latency
experienced by these tasks while judiciously utilizing the device buffering
capability. We use a dynamic programming framework to model the optimal
prefetching policy. We further propose a) a prefetching algorithm Fetch-or- Not
(FON), which uses quasi-static assumption on system state to make prefetching
decisions, and b) a prefetching policy RFON, which uses randomized
approximation to the optimal solution thus obviating the need for dynamic
online optimization and substantially reducing the computational complexity.
Through performance evaluation under slow and fast fading scenarios we show
that proposed algorithms come close to performance of the optimal scheme.
