A Random Greedy based Design Time Tool for AI Applications Component Placement andResource 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 Edge Computing (EDGE) Proceedings. 

What is included in zip file:

• Two folders: one of them (use_case_simulation) includes the code and the experimental results related to the use case mentioned in the paper. The other folder (large_scale_simulation) includes the codes and result related to the large scale scenarios
• use_case_simulation folder: Includes 2 main python files: main_paper_multiProcess.py , main_paper_singleProcess.py, in order to run the code by all processors of local machine or only single one. Note that the file results generated by them are different. To draw the plots (as they are shown in the paper), So if  main_paper_multiProcess.py was run, drow_plots_multiProcessing_results.py should be run to draw the plots while if main_paper_singleProcess.py  was run, drow_plots_singleProcessing_results.py should be run to draw the plots.
• Classes: This folder includes all classes using by the tools
• 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 and HyperOpt. Random_Greedy.json file include system descriptions related to the random greedy algorithm in which some partitions components are free to place in edge or cloud.  
• Output_Files, Output_Files1: They are the folders that when the user run main_paper_multiProcess.py or main_paper_multiProcess.py, the result will go to these folders. User can change the name of the folder but when he wants to run the code, he should give the address of the output file as an input.
• PaperResult_Output_Files folder: includes all the result files of our implementation in the paper. If user want to draw the plots in the paper, he should give this folder address to drow_plots_multiProcessing_results.py.
• large_scale_simulation folder: This folder includes some folders for different scales including 5, 10, 15 and 20 components and their system description files.

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

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