AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security
Cloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to synthesize empirical evidence on the appli...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2026
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| _version_ | 1881508987107540992 |
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| author | Ito López, Ronaldy Solano Gutierrez Oscata, Romel Condori Coaquira, Angel Rosendo Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk Tocto Cano, Esteban |
| author_browse | Condori Coaquira, Angel Rosendo Gonzales Medina, Ronny Ivan Gutierrez Oscata, Romel Ito López, Ronaldy Solano López Gonzales, Javier Linkolk Tocto Cano, Esteban |
| author_facet | Ito López, Ronaldy Solano Gutierrez Oscata, Romel Condori Coaquira, Angel Rosendo Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk Tocto Cano, Esteban |
| author_sort | Ito López, Ronaldy Solano |
| collection | Producción científica |
| description | Cloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to synthesize empirical evidence on the application of artificial intelligence (AI) and machine learning (ML) models for cost optimisation, resource management, and security enhancement in cloud computing environments. Following the PRISMA 2020 guidelines and the Kitchenham-Charters methodology, a structured search was conducted across IEEE Xplore, Web of Science, ScienceDirect, and the ACM Digital Library, covering the period 2020-2025. From an initial pool of 216 records, 18 primary studies were selected after applying the PICOC framework, predefined inclusion and exclusion criteria, and a dual-reviewer quality assessment process yielding substantial inter-rater agreement (Cohen's κ = 0.86). The synthesized evidence demonstrates that predictive provisioning systems and intelligent load-balancing mechanisms reduce operational costs by up to 85%, metaheuristic algorithms such as the Whale Optimization Algorithm and Particle Swarm Optimization improve energy efficiency by 30%-40% and increase resource utilization by up to 80%, and deep learning-based intrusion detection systems achieve accuracy levels exceeding 92%. These findings confirm that AI constitutes a structural mechanism for strengthening economic efficiency, operational resilience, and the sustainability of cloud infrastructures. However, heterogeneity in simulation environments, limited validation in production-scale deployments, and insufficient coverage of virtual machine migration dynamics represent critical gaps requiring standardized benchmarking frameworks and empirical validation in hybrid and multicloud architectures. A quantitative synthesis (Table 1) reveals that metaheuristic algorithms achieve 30%-40% cost and energy efficiency improvements, while ensemble deep learning approaches attain >97% security threat detection rates. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/390 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/3902026-11-02T12:57:04ZAI-driven optimization in cloud computing: a systematic review of cost, resource management, and securityIto López, Ronaldy SolanoGutierrez Oscata, RomelCondori Coaquira, Angel RosendoGonzales Medina, Ronny IvanLópez Gonzales, Javier LinkolkTocto Cano, EstebanArtificial intelligenceSecuritySystematic reviewMachine LearningCloud ComputingResource ManagementDeep LearningCost OptimizationCloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to synthesize empirical evidence on the application of artificial intelligence (AI) and machine learning (ML) models for cost optimisation, resource management, and security enhancement in cloud computing environments. Following the PRISMA 2020 guidelines and the Kitchenham-Charters methodology, a structured search was conducted across IEEE Xplore, Web of Science, ScienceDirect, and the ACM Digital Library, covering the period 2020-2025. From an initial pool of 216 records, 18 primary studies were selected after applying the PICOC framework, predefined inclusion and exclusion criteria, and a dual-reviewer quality assessment process yielding substantial inter-rater agreement (Cohen's κ = 0.86). The synthesized evidence demonstrates that predictive provisioning systems and intelligent load-balancing mechanisms reduce operational costs by up to 85%, metaheuristic algorithms such as the Whale Optimization Algorithm and Particle Swarm Optimization improve energy efficiency by 30%-40% and increase resource utilization by up to 80%, and deep learning-based intrusion detection systems achieve accuracy levels exceeding 92%. These findings confirm that AI constitutes a structural mechanism for strengthening economic efficiency, operational resilience, and the sustainability of cloud infrastructures. However, heterogeneity in simulation environments, limited validation in production-scale deployments, and insufficient coverage of virtual machine migration dynamics represent critical gaps requiring standardized benchmarking frameworks and empirical validation in hybrid and multicloud architectures. A quantitative synthesis (Table 1) reveals that metaheuristic algorithms achieve 30%-40% cost and energy efficiency improvements, while ensemble deep learning approaches attain >97% security threat detection rates.Articlehttps://pubmed.ncbi.nlm.nih.gov/42147039https://doi.org/10.3389/frai.2026.1750992PMC1317148510.3389/frai.2026.1750992https://hdl.handle.net/20.500.15037/39042147039W7159786708enFrontiers in Artificial Intelligence2624-82122026-04-302026-06-13T08:12:30Z2026-06-13T08:12:30Z |
| spellingShingle | Artificial intelligence Security Systematic review Machine Learning Cloud Computing Resource Management Deep Learning Cost Optimization Ito López, Ronaldy Solano Gutierrez Oscata, Romel Condori Coaquira, Angel Rosendo Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk Tocto Cano, Esteban AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security |
| title | AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security |
| title_full | AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security |
| title_fullStr | AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security |
| title_full_unstemmed | AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security |
| title_short | AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security |
| title_sort | ai driven optimization in cloud computing a systematic review of cost resource management and security |
| topic | Artificial intelligence Security Systematic review Machine Learning Cloud Computing Resource Management Deep Learning Cost Optimization |
| topic_facet | Artificial intelligence Security Systematic review Machine Learning Cloud Computing Resource Management Deep Learning Cost Optimization |
| url | https://pubmed.ncbi.nlm.nih.gov/42147039 https://doi.org/10.3389/frai.2026.1750992 https://hdl.handle.net/20.500.15037/390 |
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