We study the $k$-server problem with time-windows. In this problem, each request $i$ arrives at some point $v_i$ of an $n$-point metric space at time $b_i$ and comes with a deadline $e_i$. One of the $k$ servers must be moved to $v_i$ at some time in the interval $[b_i, e_i]$ to satisfy this request. We give an online algorithm for this problem with a competitive ratio of ${\rm polylog} (n,Δ)$, where $Δ$ is the aspect ratio of the metric space. Prior to our work, the best competitive ratio known for this problem was $O(k \cdot {\rm polylog}(n))$ given by Azar et al. (STOC 2017). Our algorithm is based on a new covering linear program relaxation for $k$-server on HSTs. This LP naturally corresponds to the min-cost flow formulation of $k$-server, and easily extends to the case of time-windows. We give an online algorithm for obtaining a feasible fractional solution for this LP, and a primal dual analysis framework for accounting the cost of the solution. Together, they yield a new $k$-server algorithm with poly-logarithmic competitive ratio, and extend to the time-windows case as well. Our principal technical contribution lies in thinking of the covering LP as yielding a {\em truncated} covering LP at each internal node of the tree, which allows us to keep account of server movements across subtrees. We hope that this LP relaxation and the algorithm/analysis will be a useful tool for addressing $k$-server and related problems.