Automated Anomaly Detection and Remediation in Serverless Container Orchestration using Adaptive Gaussian Mixture Models (AGMM)

**Abstract:** This paper proposes an innovative framework for proactive anomaly detection and automated remediation within serverless container orchestration platforms, specifically focusing on Amazon Fargate. Traditional monitoring solutions often struggle to identify subtle performance degradation and anomalous behavior within the dynamic and ephemeral nature of serverless environments. Our Adaptive Gaussian Mixture Model (AGMM) approach dynamically learns and adapts to the evolving operational characteristics of Fargate containers, enabling early and accurate detection of deviations from the expected behavior. Integrated with a Rule-Engine utilizing reinforcement learning, AGMM triggers automated remediation actions, minimizing downtime and ensuring optimal application performance. The system's immediate commercial viability stems from its ability to drastically reduce operational overhead and improve the overall reliability of modern serverless architectures. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*