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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2025
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| Acceso en línea: | https://doi.org/10.5281/zenodo.19417805 |
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| _version_ | 1866902258975768576 |
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| author | Shahnoza Akhmedova |
| author_facet | Shahnoza Akhmedova |
| contents | Autonomous Cloud Operations (ACO) represents a transformative evolution in cloud computing, where Artificial Intelligence (AI) enables self-managing, self-healing, and self-optimizing systems. Traditional cloud management relies heavily on manual intervention and rule-based automation, which struggles to cope with the increasing complexity, scale, and dynamic nature of modern cloud environments. By integrating machine learning, predictive analytics, and intelligent decision-making, ACO minimizes human involvement while improving operational efficiency, reliability, and cost optimization. AI-driven systems continuously monitor workloads, detect anomalies, predict failures, and automatically execute corrective actions. This paradigm enhances resource utilization, ensures high availability, and supports real-time scalability. Furthermore, ACO aligns with DevOps and Site Reliability Engineering (SRE) practices, enabling faster innovation and reduced downtime. Despite challenges such as data privacy, model bias, and system transparency, autonomous cloud systems are poised to redefine the future of cloud infrastructure management. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19417805 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Autonomous Cloud Operations Using Artificial Intelligence Shahnoza Akhmedova Autonomous Cloud Operations (ACO) represents a transformative evolution in cloud computing, where Artificial Intelligence (AI) enables self-managing, self-healing, and self-optimizing systems. Traditional cloud management relies heavily on manual intervention and rule-based automation, which struggles to cope with the increasing complexity, scale, and dynamic nature of modern cloud environments. By integrating machine learning, predictive analytics, and intelligent decision-making, ACO minimizes human involvement while improving operational efficiency, reliability, and cost optimization. AI-driven systems continuously monitor workloads, detect anomalies, predict failures, and automatically execute corrective actions. This paradigm enhances resource utilization, ensures high availability, and supports real-time scalability. Furthermore, ACO aligns with DevOps and Site Reliability Engineering (SRE) practices, enabling faster innovation and reduced downtime. Despite challenges such as data privacy, model bias, and system transparency, autonomous cloud systems are poised to redefine the future of cloud infrastructure management. |
| title | Autonomous Cloud Operations Using Artificial Intelligence |
| url | https://doi.org/10.5281/zenodo.19417805 |