International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 2, 8 Apr. 2026
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Cloud-native AI Pipelines for Continuous Infrastructure Optimization and Anomaly Detection
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Author(s)
Viktor Vyshnivskyi
1
Vadym Mukhin
2,*
Olha Zinchenko
3
Vitalii Kotelianets
1
Oleksandr Zvenihorodskyi
3
Pavlo Kudrynskyi
1
Oleksandr Vyshnivskyi
1
Index Terms
Cloud-native Architecture, Artificial Intelligence, Infrastructure Optimization, Anomaly Detection, Kubernetes, Machine Learning, AIOps
Abstract
The article describes a model of cloud-native AI pipelines designed for continuous optimization of computing infrastructure and real-time anomaly detection. The developed model combines modern approaches to observability, machine learning (ML), and auto-scaling based on load forecasting. The methodology is based on the use of LSTM models, autoencoders, and convolutional neural networks (CNN) integrated into Kubernetes environment with support for Prometheus, Kafka, and Grafana. Load changes are simulated, and the system's response to critical events is evaluated. The results demonstrate a significant improvement in anomaly detection accuracy (up to 93%) and resource efficiency (up to 26% cost reduction compared to traditional approaches). The proposed model can be used in AIOps systems that require a high level of automation and reliability.
Cite This Paper
Viktor Vyshnivskyi, Vadym Mukhin, Olha Zinchenko, Vitalii Kotelianets, Oleksandr Zvenihorodskyi, Pavlo Kudrynskyi, Oleksandr Vyshnivskyi, "Cloud-native AI Pipelines for Continuous Infrastructure Optimization and Anomaly Detection", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.2, pp.1-18, 2026. DOI:10.5815/ijcnis.2026.02.01
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