A Bicriteria Approximation for the Reordering Buffer Problem

TL;DR

This paper presents a bicriteria approximation algorithm for the offline reordering buffer problem, achieving near-optimal travel costs with a slightly increased buffer size, applicable to general metrics through randomized embeddings.

Contribution

It introduces a bicriteria approximation for RBP on general metrics, improving previous bounds and extending solutions beyond uniform metrics.

Findings

Achieves a 9-approximation for tree metrics with buffer 4k+1.

Implements randomized embeddings to extend results to general metrics with O(log n) cost approximation.

Improves upon the previous O(log^2 k log n) approximation for arbitrary metrics.

Abstract

In the reordering buffer problem (RBP), a server is asked to process a sequence of requests lying in a metric space. To process a request the server must move to the corresponding point in the metric. The requests can be processed slightly out of order; in particular, the server has a buffer of capacity k which can store up to k requests as it reads in the sequence. The goal is to reorder the requests in such a manner that the buffer constraint is satisfied and the total travel cost of the server is minimized. The RBP arises in many applications that require scheduling with a limited buffer capacity, such as scheduling a disk arm in storage systems, switching colors in paint shops of a car manufacturing plant, and rendering 3D images in computer graphics. We study the offline version of RBP and develop bicriteria approximations. When the underlying metric is a tree, we obtain a solution of cost no more than 9OPT using a buffer of capacity 4k + 1 where OPT is the cost of an optimal solution with buffer capacity k. Constant factor approximations were known previously only for the uniform metric (Avigdor-Elgrabli et al., 2012). Via randomized tree embeddings, this implies an O(log n) approximation to cost and O(1) approximation to buffer size for general metrics. Previously the best known algorithm for arbitrary metrics by Englert et al. (2007) provided an O(log^2 k log n) approximation without violating the buffer constraint.