  Recently, deep learning has been successfully applied to a variety of
networking problems. A fundamental challenge is that when the operational
environment for a learning-augmented system differs from its training
environment, such systems often make badly informed decisions, leading to bad
performance. We argue that safely deploying learning-driven systems requires
being able to determine, in real time, whether system behavior is coherent, for
the purpose of defaulting to a reasonable heuristic when this is not so. We
term this the online safety assurance problem (OSAP). We present three
approaches to quantifying decision uncertainty that differ in terms of the
signal used to infer uncertainty. We illustrate the usefulness of online safety
assurance in the context of the proposed deep reinforcement learning (RL)
approach to video streaming. While deep RL for video streaming bests other
approaches when the operational and training environments match, it is
dominated by simple heuristics when the two differ. Our preliminary findings
suggest that transitioning to a default policy when decision uncertainty is
detected is key to enjoying the performance benefits afforded by leveraging ML
without compromising on safety.
