AI-Driven CI/CD Pipelines Engineering for Kubernetes Based Cloud Applications
DOI:
https://doi.org/10.15662/IJEETR.2024.0601005Keywords:
AI-driven CI/CD, Kubernetes, Cloud-native applications, DevOps automation, Pipeline optimization, Continuous deployment, Predictive resource allocationAbstract
Migration requirements have also been speeded up due to the shift to cloud-native according to agile and reliable and scalable continuous integration and continuous deployment pipelines (CI/CD). The new-de-facto orchestration tool of containerized application is Kubernetes, and exercising more complicated CI/CD process under the dynamic cloud environment is difficult. Another AI-assisted pipeline design and compatible with cloud applications based on Kubernetes are also introduced in this research. The presented framework uses the machine learning to perform predictive optimization of the builds, anomalies, assigning resources to optimize the efficiency of the pipeline and minimizing its breakages. The system can react dynamically to variations in the work load, utilizing AI, the same amount of time that the system forecasts the bottlenecks in the pipeline, and then suggests rollback operations and pipelines that will run full-time with the minimum number of human supervisors. It is built upon container image management, automated testing, orchestration of deployment and real-time monitoring to introduce end-to-end visibility of a pipeline and actionable insights. Experimental assessment shows tremendous increases in the pace of deployment, resources utilization and fault tolerance when compared to conventional CI/CD deployments. Additionally, AI-based pipeline enhances scalability and resilience of microservices based to varying workloads. The work proposes an implementation-oriented and methodological perspective to the organization aiming to revolutionize the DevOps and capitalize on the capabilities which Kubernetes offers by default. The results represent the capabilities of AI-based integration in DevOps automation in which smarter self-optimizing CI/CDs systems can be developed in the cloud-native ecosystems.
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