  Many networking applications require timely access to recent network
measurements, which can be captured using a sliding window model. Maintaining
such measurements is a challenging task due to the fast line speed and scarcity
of fast memory in routers. In this work, we study the impact of allowing
\emph{slack} in the window size on the asymptotic requirements of sliding
window problems. That is, the algorithm can dynamically adjust the window size
between $W$ and $W(1+\tau)$ where $\tau$ is a small positive parameter. We
demonstrate this model's attractiveness by showing that it enables efficient
algorithms to problems such as MAX and GENERAL-SUM that require $\Omega(W)$
bits even for constant factor approximations in the exact sliding window model.
Additionally, for problems that admit sub-linear approximation algorithms such
as BASIC-SUMMING and COUNT-DISTINCT, the slack model enables a further
asymptotic improvement.
