BORA: Bayesian Optimization for Resource Allocation

TL;DR

This paper introduces BORA, a Bayesian Optimization framework for resource allocation that adapts to changing resource availability, demonstrating superior efficiency over traditional Semi-Bandit Feedback methods in both simulated and real-world scenarios.

Contribution

It extends resource allocation models to dynamic environments and proposes Bayesian Optimization with Wasserstein distance as a novel, more effective alternative to SBF.

Findings

BORA outperforms SBF in efficiency and effectiveness.

BORA adapts to changing resource availability.

Empirical results on real-life marketing optimization show improvements.

Abstract

Optimal resource allocation is gaining a renewed interest due its relevance as a core problem in managing, over time, cloud and high-performance computing facilities. Semi-Bandit Feedback (SBF) is the reference method for efficiently solving this problem. In this paper we propose (i) an extension of the optimal resource allocation to a more general class of problems, specifically with resources availability changing over time, and (ii) Bayesian Optimization as a more efficient alternative to SBF. Three algorithms for Bayesian Optimization for Resource Allocation, namely BORA, are presented, working on allocation decisions represented as numerical vectors or distributions. The second option required to consider the Wasserstein distance as a more suitable metric to use into one of the BORA algorithms. Results on (i) the original SBF case study proposed in the literature, and (ii) a real-life application (i.e., the optimization of multi-channel marketing) empirically prove that BORA is a more efficient and effective learning-and-optimization framework than SBF.