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Computer Science > Software Engineering

arXiv:2311.10767 (cs)
[Submitted on 15 Nov 2023]

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

Authors:Eneko Osaba, Gorka Benguria, Jesus L. Lobo, Josu Diaz-de-Arcaya, Juncal Alonso, Iñaki Etxaniz
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Abstract: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.
Comments: 10 pages, 5 figures, paper accepted for being presented in the upcoming 6th International Conference on Computational Intelligence and Intelligent Systems (CIIS 2023)
Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
Cite as: arXiv:2311.10767 [cs.SE]
  (or arXiv:2311.10767v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2311.10767

Submission history

From: Eneko Osaba [view email]
[v1] Wed, 15 Nov 2023 11:28:00 UTC (316 KB)
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