  Emerging software-defined networking technologies enable more adaptive
communication infrastructures, allowing for quick reactions to changes in
networking requirements by exploiting the workload's temporal structure.
However, operating networks adaptively is algorithmically challenging, as
meeting networks' stringent dependability requirements relies on maintaining
basic consistency and performance properties, such as loop freedom and
congestion minimization, even during the update process. This paper leverages
an augmentation-speed tradeoff to significantly speed up consistent network
updates. We show that allowing for a small and short (hence practically
tolerable, e.g., using buffering) oversubscription of links allows us to solve
many network update instances much faster, as well as to reduce computational
complexities (i.e., the running times of the algorithms). We first explore this
tradeoff formally, revealing the computational complexity of scheduling
updates. We then present and analyze algorithms that maintain logical and
performance properties during the update. Using an extensive simulation study,
we find that the tradeoff is even more favorable in practice than our
analytical bounds suggest. In particular, we find that by allowing just 10%
augmentation, update times reduce by more than 32% on average, across a
spectrum of real-world networks.
