EdgeTB: A Hybrid Testbed for Distributed Machine Learning at the Edge With High Fidelity

Distributed Machine Learning (DML) at the edge has become an essential topic for providing low-latency intelligence near the data sources. However, both the development and testing of DMLs lack sufficient support. Reusable libraries that abstract the general functionalities of DMLs are needed for rapid development. Moreover, existing physical testbeds are usually small and lack network flexibility, while virtual testbeds like simulators and emulators lack fidelity. This paper proposes a novel hybrid testbed EdgeTB, which provides numerous emulated nodes to generate large-scale and network-flexible test environments while incorporating physical nodes to guarantee fidelity. EdgeTB manages physical nodes and emulated nodes uniformly and supports arbitrary network topologies between nodes through dynamic configurations. Importantly, we propose Role-oriented development to support the rapid development of DMLs. Through case studies and experiments, we demonstrate that EdgeTB provides convenience for efficiently developing and testing DMLs in various structures with high fidelity and scalability.