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Simulating multi-agent-based computation offloading for autonomous cars

Abstract : Efficient task processing and data storage are still among the most important challenges in Autonomous Driving (AD). In-board processing units struggle to deal with the workload of AD tasks, especially for Artificial Intelligence (AI) based applications. Cloud and Fog computing represent good opportunities to overcome the limitation of in-board processing capacities. However, communication delays and task real-time constraints are the main issues to be considered during the task mapping. Also, a fair resources allocation is a miss-explored concept in the context of AD task offloading where the mobility increases its complexity. We propose a task offloading simulation tool and approaches based on intelligent agents. Agents at the edge and the fog communicate and exchange their knowledge and history. We show results and proof-of-concept scenarios that illustrate our multi-agent-based proposition and task offloading simulation tool. We also analyze the impact of communication delays and processing units constraints on AD task offloading issues
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Contributor : Mylène Delrue Connect in order to contact the contributor
Submitted on : Monday, January 17, 2022 - 9:12:41 AM
Last modification on : Monday, June 27, 2022 - 2:31:00 PM




Hamza Ouarnoughi, Emmanuelle Grislin-Le Strugeon, Smail Niar. Simulating multi-agent-based computation offloading for autonomous cars. Cluster Computing, Springer Verlag, 2021, pp.17. ⟨10.1007/s10586-021-03440-y⟩. ⟨hal-03528052⟩



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