A Hitting Set Relaxation for $k$-Server and an Extension to Time-Windows

TL;DR

This paper introduces a new online algorithm for the $k$-server problem with time-windows, achieving a polylogarithmic competitive ratio by using a novel LP relaxation and primal-dual analysis, extending previous bounds.

Contribution

The authors develop a new covering LP relaxation for $k$-server on HSTs that extends to time-windows, enabling a polylogarithmic competitive ratio for the online problem.

Findings

Achieved a polylogarithmic competitive ratio for $k$-server with time-windows.

Extended the LP relaxation and primal-dual framework to handle time-windows.

Provided a new analysis technique based on truncated covering LPs at tree nodes.

Abstract

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 ${\rm polylog} (n,\Delta)$, where $\Delta$ 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.