This paper investigates passive bandwidth estimation as ameans to improve scheduling in Kubernetes-based fog computing environments.In contrast to active probing techniques, passive methodsrely on observing existing traffic patterns to infer available bandwidth(ABW), offering a non-intrusive alternative suitable for dynamicand distributed systems. The study focuses on wired infrastructure networkswithin the cloud–fog–edge continuum. We develop and present an approach based on a modified version ofthe probe gap model, evaluated in an emulated network testbed. Tenexperiments were conducted under varying traffic conditions, packet lossrates, and link capacities. Timestamping artifacts and data filtering wereaddressed to ensure measurement integrity. The approach demonstratesreliable ABW estimation across all scenarios, confirming the viability ofpassive methods for bandwidth-aware scheduling in Kubernetes.