Real-Time Risk Forecasting in Serverless DevOps: A Meta-Learning Approach

Abstract

Serverless structures have changed how software is made, carried out, and extended in a cutting-edge cloud-native setting. Nevertheless, serverless systems’ abstract and dynamic natures create significant problems regarding their operations’ reliability, security, and efficiency. The real-time requirements and unpredictable behavior of such environments cannot be met with traditional risk management strategies. The novel meta-learning system in the paper introduces a real-time risk prediction in serverless DevOps pipelines. Our system also quickly applies to new risks and anomalies and uses little data because of few-shot learning methods that similar systems do not use compared to a static model in which there is significant system retraining. We propose a modular pipeline that intercepts with CI/CD pipelines, consuming telemetry signals of runtime performance, logs, and deployment traces to predict the essential risks like function timeouts, cold starts, misconfigurations, and threats of security violations. In simulated and real-life implementation circumstances, we have demonstrated that meta-learning has much better accuracy and response times. Our solution minimizes the mean time to resolution (MTTR), allows proactive remediation, and improves general system resilience. The work introduces the basis of innovative and reactive DevSecOps to be deployed in the serverless environment and underlines the disruptive potential of meta-learning to manage operational risk.