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Computer Science > Machine Learning

arXiv:2210.05977 (cs)
[Submitted on 12 Oct 2022]

Title:BORA: Bayesian Optimization for Resource Allocation

Authors:Antonio Candelieri, Andrea Ponti, Francesco Archetti
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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.
Comments: 31 pages, 12 figures
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
Cite as: arXiv:2210.05977 [cs.LG]
  (or arXiv:2210.05977v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2210.05977

Submission history

From: Antonio Candelieri [view email]
[v1] Wed, 12 Oct 2022 07:39:05 UTC (247 KB)
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