Automating the Deployment of Artificial Intelligence Services in Multiaccess Edge Computing Scenarios

With the increasing adoption of the edge computing paradigm, including multi-access edge computing (MEC) in telecommunication scenarios, many works have explored the benefits of adopting it. Since MEC, in general, presents a reduction in latency and energy consumption compared to cloud computing, it has been applied to deploy artificial intelligence services. This kind of service can have distinct requirements, which involve different computational resource capabilities as well different data formats or communication protocols to collect data. In this sense, we propose the VEF Edge Framework, which aims at helping the development and deployment of artificial intelligence services for MEC scenarios considering requirements as low-latency and CPU/memory consumption. We explain the VEF architecture and present experimental results obtained with a base case's implementation: an object detection inference service deployed with VEF. The experiments measured CPU and memory usage for the VEF's main components and the processing time for two procedures (inference and video stream handling).