A Randomized Greedy Method for AI Applications Component Placement and Resource Selection in Computing Continua

This is the source code and dataset that we used in the paper: "A Randomized Greedy Method for AI Applications Component Placement and Resource Selection in Computing Continua" which is published in 2021 IEEE International Conference on Joint Cloud Computing (JCC) Proceedings. 

What is included in zip file:

• Two main python files: main.py is the python file related to the first section of experimental results which runs the code related to the paper plots. main_larg_scale.py is the python file related to the second section of the experimental results which run random greedy for some different number of components as reported in the "scalability analysis" subsection.
• ConfigFiles: this folder includes all necessary input files which are json files. Input_file.json includes input lambda interval and step, the Bandwidth Scenario of network domain 2 (ND2) and the number of iterations in the proposed random greedy algorithm. Random_Greedy.json file include system descriptions related to the random greedy algorithm in which some components are free to place in edge or cloud. OnlyCloud.json file includes system descriptions related to cloud only scenario in which some components can be placed only on cloud VMs. OnlyEdge.json file includes system descriptions related to edge only scenario in which some components can be placed only on edge devices.
• large_scale: this folder include the system description files for different scales including 5, 10, 15 and 20 components.
• Output_files: After running the code, the output will be saved in this folder that is include the plots and best solutions for lambdas intervals.

The official version of this tool is located in the following link: 

https://gitlab.polimi.it/ai-sprint/space4ai-d