PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Effectiveness of AI-Based Resource Optimization in Cloud Environments: An Empirical Study Focused on Comparative Analysis between MSA and Rehost Systems
Version 1
: Received: 17 March 2024 / Approved: 18 March 2024 / Online: 18 March 2024 (11:37:55 CET)
How to cite:
Yoo, D.J.; Seo, C.J. Effectiveness of AI-Based Resource Optimization in Cloud Environments: An Empirical Study Focused on Comparative Analysis between MSA and Rehost Systems. Preprints2024, 2024031030. https://doi.org/10.20944/preprints202403.1030.v1
Yoo, D.J.; Seo, C.J. Effectiveness of AI-Based Resource Optimization in Cloud Environments: An Empirical Study Focused on Comparative Analysis between MSA and Rehost Systems. Preprints 2024, 2024031030. https://doi.org/10.20944/preprints202403.1030.v1
Yoo, D.J.; Seo, C.J. Effectiveness of AI-Based Resource Optimization in Cloud Environments: An Empirical Study Focused on Comparative Analysis between MSA and Rehost Systems. Preprints2024, 2024031030. https://doi.org/10.20944/preprints202403.1030.v1
APA Style
Yoo, D.J., & Seo, C.J. (2024). Effectiveness of AI-Based Resource Optimization in Cloud Environments: An Empirical Study Focused on Comparative Analysis between MSA and Rehost Systems. Preprints. https://doi.org/10.20944/preprints202403.1030.v1
Chicago/Turabian Style
Yoo, D.J. and Cheong Jeong Seo. 2024 "Effectiveness of AI-Based Resource Optimization in Cloud Environments: An Empirical Study Focused on Comparative Analysis between MSA and Rehost Systems" Preprints. https://doi.org/10.20944/preprints202403.1030.v1
Abstract
This study empirically analyzes the effectiveness of resource optimization utilizing artificial intelligence (AI) within cloud computing environments. It compares systems that have been transitioned from a Monolithic structure to Microservices Architecture (MSA) with those adopting a simple Rehost strategy, to validate the operational efficiency of two systems whose resource efficiency has been enhanced compared to traditional legacy systems. The research is based on systems that determined resource efficiency post-cloud migration using APM tools. It points out the limitations of traditional heuristic operation methods in resource optimization, which lead to issues such as application performance degradation, resource over-allocation, and increased operational costs. This study aims to overcome the conventional resource optimization standards that depended on the average peak usage of CPU and memory infrastructure, by introducing AI-based optimization tools, and applying on-premise standards to cloud and container environments to achieve resource optimization. The paper emphasizes the importance of resource optimization through AI-based automation tools, exploring the possibilities of resolving real-world application performance issues in cloud environments, reducing unnecessary resource usage costs, and cutting down on labor risks and operational expenses. Through this, it presents strategic directions on how AI technology can offer financial and operational advantages in managing cloud resources.
Keywords
Cloud Resourece Optimization; AI-Based Automation in Cloud Computing; Performance Management in Cloud Environments; Cost Efficiency in Cloud Services; Turbonomic ARM
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
