AI Report - Federated AIOps for Multi-Cluster OpenShift
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I2P111Keywords:
Federated Aiops, Multi-Cluster Openshift, Anomaly Detection, Mean Time To Resolve (MTTR), Root Cause Analysis (RCA)Abstract
Multi-cluster OpenShift systems produce large volumes of data, containing as many as 400-000 metrics and 200GB of logs each day per cluster, overwhelming operations groups. This study shows that Mean Time to Detect (MTTD) decreases by 40-60%, Mean Time to Resolve (MTTR) reduces by 25-55%, and the number of alerts drops by 70% when Federated AIOps is integrated. With the support of a federated AIOps model on OpenShift RHACM, Prometheus, OpenTelemetry, and a cross-cluster machine learning control plane, this research proposes an inferential approach of automation when passing anomaly detection and root cause analysis (RCA) across geographically separated clusters. The study employed data on six clusters, including AWS, Azure, and on-prem systems, 600 + nodes, 12,000 + pods, and 350 microservices. The findings indicate that Federated AIOps had a 94.3% accuracy in data correlations, false-positive alerts to less than 6 occurrences, and less than 2.5% of CPU overhead per node. Federated AIOps can reduce alert duplication and optimize incident management, which provides an open-source alternative to official AIOps platforms that makes it a low-cost, scalable product in the large-scale setting. This study identifies the practical effect of Federated AIOps in improving operational efficiency, resource utilization, and adherence to data privacy standards and generates significant cost reduction in the multi-cluster OpenShift administration
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