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Optimizing IaC Configurations: a Case Study Using Nature-inspired Computing

arXiv 2311.10767 ↗First posted 15 Nov 2023cs.SE cs.AIcited by 0

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Abstract (arXiv)

In the last years, one of the fields of artificial intelligence that has been investigated the most is nature-inspired computing. The research done on this specific topic showcases the interest that sparks in researchers and practitioners, who put their focus on this paradigm because of the adaptability and ability of nature-inspired algorithms to reach high-quality outcomes on a wide range of problems. In fact, this kind of methods has been successfully applied to solve real-world problems in heterogeneous fields such as medicine, transportation, industry, or software engineering. Our main objective with this paper is to describe a tool based on nature-inspired computing for solving a specific software engineering problem. The problem faced consists of optimizing Infrastructure as Code deployment configurations. For this reason, the name of the system is IaC Optimizer Platform. A prototypical version of the IOP was described in previous works, in which the functionality of this platform was introduced. With this paper, we take a step forward by describing the final release of the IOP, highlighting its main contribution regarding the current state-of-the-art, and justifying the decisions made on its implementation. Also, we contextualize the IOP within the complete platform in which it is embedded, describing how a user can benefit from its use. To do that, we also present and solve a real-world use case.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 1 of its 23 references that are in the map. First posted on arXiv 15 Nov 2023.

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References · 1

  1. 2018Where Are The Gaps? A Systematic Mapping Study of Infrastructure as Code Researchcited by 9

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