AI Applications Resource Allocation in Computing Continuum: a Stackelberg Game Approach
Year: 2023
Type: software
Abstract: This project consists of the code and Appendix related to the work "Resource Allocation to AI Applications in Computing Continuum: a Stackelberg Game Approach". Appendix.pdf is the Appendix of the paper. The ConfigFile folder includes all the .json file templates for the system and users models. The classes folder includes all classes and functions that are needed. Profit_ratio.py file is used to compute the extra-profit and costs saving of the edge platform by solving the game, instead of imposing a fixed price. Draw_plots.py file is used to plot the users and platform profit functions. Heuristic_VS_Baron.py is used to compare the performance of the full knowledge, heuristic algorithm with the state-of-the-art BARON solver. Main_MEC.py file is the main file to run the project. Partial_knowledge_users_parameters.py file is used to run the Follow the Line algorithm and to compare the results with the full knowledge scenario. Test_heuristic.py file is used to compare the different versions of the heuristic approach, in the full knowledge scenario. Utility.py file contains some useful functions to check and analyse the solutions.
Author: Roberto Sala
Institution: Politecnico di Milano
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6894116274
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