Chapitre D'ouvrage Année : 2024

Fog-Robotics Infrastructures Simulation-Based Sizing Approach

Résumé

Nowadays, robots are increasingly being used to facilitate the automation of human tasks in various sectors and domains. These robots often have limited resources (memory, storage, computing power) for their activities (such as processing an AI service). In such cases, it is common to use on-demand architectures like Cloud architectures to offload the robot’s data management and optimize its operations (known as Robot-to-Cloud or R2C architectures). However, sizing such architectures involves solving highly complex optimization problems or comparing numerous hypothetical and costly scenarios in terms of time and/or money for deployment in a real environment. To address this challenge, we propose an approach called FRISSA (Fog-Robotics Infrastructure Simulation-based Sizing Approach) in this article. It estimates resource requirements in Cloud-based environments based on simulation results obtained with SimGrid. The approach specifically focuses on homogeneous and static Fog Robotics infrastructures. It introduces an algorithm that simultaneously optimizes the duration and cost of using Fog resources to determine the minimal resource requirements that compromise both metrics. We apply this approach to an existing robotics use case, which involves deploying a human face detection service on the Cloud for a stream of images.
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Dates et versions

hal-04807591 , version 1 (27-11-2024)

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Lucien Ngale, Eddy Caron, Yulin Zhang. Fog-Robotics Infrastructures Simulation-Based Sizing Approach. Cloud Computing and Services Science : 12th International Conference, CLOSER 2022, Virtual Event, April 27–29, 2022, and 13th International Conference, CLOSER 2023, Prague, Czech Republic, April 26–28, 2023, Revised Selected Papers, 1845, Springer Nature Switzerland, pp.188-211, 2024, Communications in Computer and Information Science, 978-3-031-68165-3. ⟨10.1007/978-3-031-68165-3_10⟩. ⟨hal-04807591⟩
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