Configuring the Parallel Committees Database Architecture through Bayesian Optimization

Distributed databases are often used when scalability, fault tolerance, and high availability are crucial. They excel in scenarios where traditional, centralized databases may struggle to handle the increasing volume of data or traffic. Common use cases include large-scale web applications, cloud computing environments, and systems requiring geographic distribution for improved performance and resilience. The "Parallel Committees" architecture was recently proposed, designed, and published in [1] as an innovative and novel approach for distributed databases. The configuration of the Parallel Committees architecture must be precisely tailored based on a set of input parameters. In this paper, we propose an approach to configure the Parallel Committees database architecture through Bayesian optimization, aiming to identify the optimal parameter configuration for each specific use case.