  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.

Machine Learning;Artificial Intelligence;Optimization and Control