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A Multi-Agent Approach for Vehicle-to-Fog Fair Computation Offloading

Abstract : Future autonomous driving (AD) will require more data processing and storage capacities, exceeding the in-board capacities, especially with AI application requirements. The cloud or fog resources can provide off-load services. But to obtain an optimized task distribution between the on-board edge computing platform and cloud or Fog resources, communication delays and AD real-time constraints must be taken into account. The equity in resource allocation is rarely studied in this context where mobility adds a specific difficulty. Our approach of offloading mechanism is to leverage intelligent agents able to make decisions on task delegation based on past decisions. Agents at edge and fog levels communicate and exchange their knowledge and past decisions. A first scenario illustrates the proposition with simulated data.
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https://hal-uphf.archives-ouvertes.fr/hal-03382302
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Submitted on : Monday, October 18, 2021 - 10:35:41 AM
Last modification on : Wednesday, November 3, 2021 - 6:29:43 AM

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Emmanuelle Grislin-Le Strugeon, Hamza Ouarnoughi, Smail Niar. A Multi-Agent Approach for Vehicle-to-Fog Fair Computation Offloading. 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), Nov 2020, Antalya, Turkey. pp.1-8, ⟨10.1109/AICCSA50499.2020.9316512⟩. ⟨hal-03382302⟩

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