Multiobjective Optimization Analysis for Finding Infrastructure-as-Code Deployment Configurations

• URL: http://arxiv.org/abs/2401.09983v1
• Date: Thu, 18 Jan 2024 13:55:32 GMT
• Title: Multiobjective Optimization Analysis for Finding Infrastructure-as-Code Deployment Configurations
• Authors: Eneko Osaba, Josu Diaz-de-Arcaya, Juncal Alonso, Jesus L. Lobo, Gorka Benguria and I\~naki Etxaniz
• Abstract summary: This paper is focused on a multiobjective problem related to Infrastructure-as-Code deployment configurations. We resort in this paper to nine different evolutionary-based multiobjective algorithms. Results obtained by each method after 10 independent runs have been compared using Friedman's non-parametric tests.
• Score: 0.3774866290142281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiobjective optimization is a hot topic in the artificial intelligence and operations research communities. The design and development of multiobjective methods is a frequent task for researchers and practitioners. As a result of this vibrant activity, a myriad of techniques have been proposed in the literature to date, demonstrating a significant effectiveness for dealing with situations coming from a wide range of real-world areas. This paper is focused on a multiobjective problem related to optimizing Infrastructure-as-Code deployment configurations. The system implemented for solving this problem has been coined as IaC Optimizer Platform (IOP). Despite the fact that a prototypical version of the IOP has been introduced in the literature before, a deeper analysis focused on the resolution of the problem is needed, in order to determine which is the most appropriate multiobjective method for embedding in the IOP. The main motivation behind the analysis conducted in this work is to enhance the IOP performance as much as possible. This is a crucial aspect of this system, deeming that it will be deployed in a real environment, as it is being developed as part of a H2020 European project. Going deeper, we resort in this paper to nine different evolutionary computation-based multiobjective algorithms. For assessing the quality of the considered solvers, 12 different problem instances have been generated based on real-world settings. Results obtained by each method after 10 independent runs have been compared using Friedman's non-parametric tests. Findings reached from the tests carried out lad to the creation of a multi-algorithm system, capable of applying different techniques according to the user's needs.

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