CLOUD-NATIVE ENGINEERING AND AI-POWERED AUTOMATION FOR SCALABLE PLATFORMS

Abstract

The paper will discuss how the combination of cloud-native engineering and AI-enabled automation can be collaboratively integrated to develop scalable, resilient, and cost-effective platforms. It explores important technologies, including microservices, containers, Kubernetes, and AIOps, with a focus on aspects such as elasticity, cost, compliance, and resilience. Its study is based on a literature review, a case study, a survey of 150 IT professionals, and experimental evidence of their efficiency. Auto scaling with one million parallel concurrencies resulted in a 30 percent reduction in reaction time and eliminated downtime by 80 percent. Cloud-native services achieved an average response time of 110 ms, which was faster than that of additional monolithic systems, which had a response time of 180 ms. The use of IBM Watson AIOps resulted in a 40% reduction in manual interventions, a 25% decrease in operational costs, and a significant decrease in incidents through the prediction and automatic remediation of anomalies. The hybrid experiment of Google Cloud on-premises security also enhanced the ISO 27001/GDPR compliance rate by 15%. It lowered the breach risk rate by 25 percent, while reducing recovery time to a mean of 4.2 minutes. DevOps pipelines (Jenkins, Terraform, GitLab, Kubernetes) were able to improve feedback and process burst workloads, aiding proactive scaling that used scaling proportionate to the load, thereby supporting the achievement of SLOs. The results indicate that cloud-natively engineered and AI-driven process automation yields cost-effective scaling, high availability, and audit-governability, making it suitable for scalable and consumer-facing regulated platforms. Future studies must incorporate the combination of deep and reinforcement learning in self-healing systems, leveraging edge computing support within autonomous systems and the Industrial Internet of Things to enable decision-making in milliseconds.