Online Allocation Problem with Two-sided Resource Constraints

TL;DR

This paper introduces a nearly optimal online algorithm for resource allocation problems with both lower and upper bounds, providing high-probability guarantees and a new measure of problem difficulty.

Contribution

It proposes a novel online algorithm framework for two-sided resource constraints, with theoretical guarantees and a new feasibility measure $\xi^*$ to evaluate problem hardness.

Findings

The algorithm achieves near-optimal competitive ratios.

It introduces a feasibility measure $\xi^*$ for problem hardness.

Provides high-probability guarantees for two-sided constraints.

Abstract

In this paper, we investigate the online allocation problem of maximizing the overall revenue subject to both lower and upper bound constraints. Compared to the extensively studied online problems with only resource upper bounds, the two-sided constraints affect the prospects of resource consumption more severely. As a result, only limited violations of constraints or pessimistic competitive bounds could be guaranteed. To tackle the challenge, we define a measure of feasibility $\xi^*$ to evaluate the hardness of this problem, and estimate this measurement by an optimization routine with theoretical guarantees. We propose an online algorithm adopting a constructive framework, where we initialize a threshold price vector using the estimation, then dynamically update the price vector and use it for decision-making at each step. It can be shown that the proposed algorithm is $\big(1-O(\frac{\varepsilon}{\xi^*-\varepsilon})\big)$ or $\big(1-O(\frac{\varepsilon}{\xi^*-\sqrt{\varepsilon}})\big)$ competitive with high probability for $\xi^*$ known or unknown respectively. To the best of our knowledge, this is the first result establishing a nearly optimal competitive algorithm for solving two-sided constrained online allocation problems with a high probability of feasibility.