(Wydawnictwo Politechniki Łódzkiej, 2023) Cłapa, Konrad; Grudzień, Krzysztof; Sierszeń, Artur
This article presents the results of an experiment performed on a
machine learning edge computing platform composed of a virtualized environment
with a K3s cluster and Kubeflow software. The study aimed to analyze
the effectiveness of executing Kubeflow pipelines for simulated parallel
executions. A benchmarking environment was developed for the experiment
to allow system performance measurements based on parameters, including
the number of pipelines and nodes. The results demonstrate the impact of
the number of cluster nodes on computational time, revealing insights that
could inform future decisions regarding increasing the effectiveness of running
machine learning pipelines on edge devices.
