Resource Variability Telemetry in Edge Computing

Edge computing is employed as a common execution platform for a variety of network functions and user applications. Consequently, the platform needs to satisfy a variety of execution time requirements. Towards a reliable edge computing, this study proposes a method to detect the factors that influence the variability of task execution time at the edge. A set of Single Particle Analysis (SPA) algorithms for an electro-microscopy use case are executed in a Kubernetes cluster, monitored by Prometheus. Our experiments show a big increase on the variability of the round trip time of a task running over shared resources. Our methodology selects the most related performance metrics among 554 captured metrics, where the correlation reaches 87%.