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.

Managing the Resource Continuum in a Real Video Surveillance Scenario

Sciamanna, Filippo;Zanella, Michele;Massari, Giuseppe;Fornaciari, William
2021

Abstract

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.
2021 24th Euromicro Conference on Digital System Design (DSD)
978-1-6654-2703-6
Fog Computing , Resource Management , Programming model , Task allocation strategy
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1188064
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