  Emerging networked systems become increasingly flexible and reconfigurable.
This introduces an opportunity to adjust networked systems in a demand-aware
manner, leveraging spatial and temporal locality in the workload for online
optimizations. However, it also introduces a trade-off: while more frequent
adjustments can improve performance, they also entail higher reconfiguration
costs.
  This paper initiates the formal study of linear networks which self-adjust to
the demand in an online manner, striking a balance between the benefits and
costs of reconfigurations. We show that the underlying algorithmic problem can
be seen as a distributed generalization of the classic dynamic list update
problem known from self-adjusting datastructures: in a network, requests can
occur between node pairs. This distributed version turns out to be
significantly harder than the classical problem in generalizes. Our main
results are a $\Omega(\log{n})$ lower bound on the competitive ratio, and a
(distributed) online algorithm that is $O(\log{n})$-competitive if the
communication requests are issued according to a linear order.
