  We consider the latency minimization problem in a task-offloading scenario,
where multiple servers are available to the user equipment for outsourcing
computational tasks. To account for the temporally dynamic nature of the
wireless links and the availability of the computing resources, we model the
server selection as a multi-armed bandit (MAB) problem. In the considered MAB
framework, rewards are characterized in terms of the end-to-end latency. We
propose a novel online learning algorithm based on the principle of optimism in
the face of uncertainty, which outperforms the state-of-the-art algorithms by
up to ~1s. Our results highlight the significance of heavily discounting the
past rewards in dynamic environments.
