Managing the Resource Continuum in a Real Video Surveillance Scenario

Over the last years, the number of IoT devices has grown exponentially. Thus, Fog and Edge computing move part of the computation closer to data sources, exploiting interconnected devices in a computing continuum viewpoint. These devices are heterogeneous in terms of performance, features, and capabilities, requiring proper programming models and run-time management layers. This work presents a first version of the BarMan open-source framework. We developed a run-time task allocation policy to maximize application performance and through an experimental evaluation performed on a real cluster, we evaluated different execution scenarios. The results show an improvement up to 66% on the frame processing latency with respect to a monolithic solution.