Authors - Muhamad Surya Nugraha, Dedy Rahman Wijaya, Tuntun Aditara Maharta Abstract - The widespread adoption of Kubernetes for orchestrating micro services has heightened monitoring complexity if we focus on identifying per formance degradation not visible at the level of infrastructure resource utiliza tion. In this paper, we present an application-centric AIOps framework that can be leveraged to detect “high-latency, low-resource” anomalies in Kubernetes microservices. Traditional autoscaling mechanisms that only rely on resource metrics (CPU and memory) fail to provide optimum response time with the emergence of reactive applications. The model for anomaly detection is trained using machine learning classifiers such as Random Forest, LightGBM, and Lo gistic Regression. This approach leads to a weak supervision-based approach to label datasets, with respect to Service Level Objective (SLO) violations. A course registration system is proposed to validate the application of this frame work under conditions of high concurrency and parallel simulation traffic. Ex perimental results show that the established machine learning model exhibits marked performance compared with normal threshold methods, leading to im proved operational steadiness and service robustness.
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