  Enhanced processing power in the cloud allows constrained devices to offload
costly computations: for instance, complex data analytics tasks can be computed
by remote servers. Remote execution calls for a new compression paradigm that
optimizes performance on the analytics task within a rate constraint, instead
of the traditional rate-distortion framework which focuses on source
reconstruction. This paper considers a simple binary hypothesis testing
scenario where the resource constrained client (transmitter) performs
fixed-length single-shot compression on data sampled from one of two
distributions; the server (receiver) performs a hypothesis test on multiple
received samples to determine the correct source distribution. To this end, the
task-aware compression problem is formulated as finding the optimal source
coder that maximizes the asymptotic error performance of the hypothesis test on
the server side under a rate constraint. A new source coding strategy based on
a greedy optimization procedure is proposed and it is shown that that the
proposed compression scheme outperforms universal fixed-length single-shot
coding scheme for a range of rate constraints.
