Fairness-Regularized Online Optimization with Switching Costs

TL;DR

This paper introduces FairOBD, a novel online optimization algorithm that balances fairness, switching costs, and action smoothness, with theoretical guarantees and empirical validation in resource provisioning.

Contribution

It proposes a new fairness-regularized online optimization framework with a competitive ratio analysis and a practical algorithm, addressing long-term fairness challenges.

Findings

FairOBD reduces fairness-regularized costs effectively.

Theoretical guarantees for competitive ratio against a new benchmark.

Empirical results show improved fairness outcomes in resource provisioning.

Abstract

Fairness and action smoothness are two crucial considerations in many online optimization problems, but they have yet to be addressed simultaneously. In this paper, we study a new and challenging setting of fairness-regularized smoothed online convex optimization with switching costs. First, to highlight the fundamental challenges introduced by the long-term fairness regularizer evaluated based on the entire sequence of actions, we prove that even without switching costs, no online algorithms can possibly achieve a sublinear regret or finite competitive ratio compared to the offline optimal algorithm as the problem episode length $T$ increases. Then, we propose FairOBD (Fairness-regularized Online Balanced Descent), which reconciles the tension between minimizing the hitting cost, switching cost, and fairness cost. Concretely, FairOBD decomposes the long-term fairness cost into a sequence of online costs by introducing an auxiliary variable and then leverages the auxiliary variable to regularize the online actions for fair outcomes. Based on a new approach to account for switching costs, we prove that FairOBD offers a worst-case asymptotic competitive ratio against a novel benchmark -- the optimal offline algorithm with parameterized constraints -- by considering $T\to\infty$. Finally, we run trace-driven experiments of dynamic computing resource provisioning for socially responsible AI inference to empirically evaluate FairOBD, showing that FairOBD can effectively reduce the total fairness-regularized cost and better promote fair outcomes compared to existing baseline solutions.