Near-Optimal Online Algorithms for Dynamic Resource Allocation Problems

TL;DR

This paper introduces near-optimal online algorithms for a broad class of dynamic resource allocation problems, including advertising and revenue management, that adapt without prior request volume knowledge.

Contribution

It presents a novel model enabling the design of learning-based online algorithms that are near-optimal and do not require prior request volume information.

Findings

Algorithms achieve near-optimal performance under mild assumptions.

The model extends to variants with relaxed initial assumptions.

First algorithms of their kind that adapt without knowing total request count.

Abstract

In this paper, we study a general online linear programming problem whose formulation encompasses many practical dynamic resource allocation problems, including internet advertising display applications, revenue management, various routing, packing, and auction problems. We propose a model, which under mild assumptions, allows us to design near-optimal learning-based online algorithms that do not require the a priori knowledge about the total number of online requests to come, a first of its kind. We then consider two variants of the problem that relax the initial assumptions imposed on the proposed model.