Identifying and Visualizing Key Scenarios in a Serverless Edge-Node System

The growing use of IoT and edge computing has highlighted the need for more efficient monitoring and visualization. These distributed systems have significant potential for lightweight, real-time monitoring, but face critical challenges, including inconsistent logging and performance tracking. Addressing these gaps is essential to optimize energy use and improve system performance in IoT-driven infrastructures. The objective of this paper is to present a solution in the form of an intelligent monitoring framework that is capable of recognizing and visualizing key operational scenarios in serverless edge devices within a clustered environment. By leveraging system logs and performance metrics, our approach provides a smart dashboard that aggregates, analyzes, and visualizes data in real-time, offering insights into system behavior. Our implementation demonstrates the ability to detect various scenarios using a Kubernetes-based cluster management system and data collection from Loki and Prometheus, along with visualization from Grafana. These capabilities include normal operation, overload, error handling, and load balancing scenarios.