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

Abstract

This paper addresses the challenges of monitoring and visualizing cognitive cloud continuum environments by making use of serverless edge nodes. The distributed nature of such nodes has the potential of granting quick and lightweight monitoring capabilities, once relevant challenges are solved, such as consistent monitoring and logging. We focus on recognizing and visualizing a set of key scenarios over serverless edge devices on a cluster. Our solution leverages system logs and metrics collected on the cluster, which are aggregated, analyzed and visualized on a smart dashboard in real-time to produce a comprehensive view of the system’s behavior. The approached is experimented with on a serverless edge-node system integrated in Kubernetes, paired with Loki and Prometheus for log and metrics collection and aggregation, and Grafana for visualization. Our implementation demonstrates the system’s ability to recognize key scenarios such as normal state, overload state, error handling, and load balancing, thereby enhancing the ability of system administrators and developers to oversee and optimize the performance of serverless edge environments. The insights provided by our research will support the development of more robust, efficient, and adaptive monitoring solutions, ultimately raising awareness of the potential of the Cognitive Cloud Continuum. Index Terms— Edge Computing, Serverless Computing, Kubernetes, Dashboard, Workloads