An Optimal Randomized Online Algorithm for Reordering Buffer Management

TL;DR

This paper presents a new randomized online algorithm for reordering buffer management with an optimal competitive ratio of O(log log k), combining LP relaxation and randomized rounding in a novel online primal-dual framework.

Contribution

It introduces an online primal-dual based approach with a two-stage process for reordering buffer management, achieving an optimal competitive ratio matching the lower bound.

Findings

Achieves an O(log log k)-competitive ratio for buffer management.

Combines primal-dual schema with LP relaxation and randomized rounding.

Matches the lower bound established in prior work.

Abstract

We give an $O(\log\log k)$-competitive randomized online algorithm for reordering buffer management, where $k$ is the buffer size. Our bound matches the lower bound of Adamaszek et al. (STOC 2011). Our algorithm has two stages which are executed online in parallel. The first stage computes deterministically a feasible fractional solution to an LP relaxation for reordering buffer management. The second stage "rounds" using randomness the fractional solution. The first stage is based on the online primal-dual schema, combined with a dual fitting argument. As multiplicative weights steps and dual fitting steps are interleaved and in some sense conflicting, combining them is challenging. We also note that we apply the primal-dual schema to a relaxation with mixed packing and covering constraints. We pay the $O(\log\log k)$ competitive factor for the gap between the computed LP solution and the optimal LP solution. The second stage gives an online algorithm that converts the LP solution to an integral solution, while increasing the cost by an O(1) factor. This stage generalizes recent results that gave a similar approximation factor for rounding the LP solution, albeit using an offline rounding algorithm.