The Heterogeneous-Aware scheduler of the ELEGANT project responsible for finding optimal placements for the user submitted applications.
We highly suggest using a virtual environment for the installation of the project.
Note that some packages like ProGraML are developed for UNIX OS only, and hence the Planner is expected to work on these type of operating systems.
Python versions expected to be compatible >=3.6 & <3.11 .
git clone https://github.com/elegant-h2020/ELEGANT-Planner.git
cd ELEGANT-Planner
pip install -r requirements.txt
To launch the Planner all you have to do is run python3 api.py on your current terminal and open a second one to run your tests. It is highly possible that during the first run of the api.py an error will occur regarding the dgl library that ProGraML is using. This is not an error of the Planner, but it prevents from running it eitherway.
More specifically the error will conclude to something like this ImportError: cannot import name DGLHeteroGraph from dgl.heterograph. This happens because DGLHeteroGraph has been deprecated and replaced by DGLGraph. In order to fix this you have to run vim path/to/lib/python/packages/programl/transform_ops.py and change DGLHeteroGraph to DGLGraph at the designated lines.
After that, rerun python3 api.py.
Assuming Flask app is active, open a second terminal and run cd tests.
In the tests directory you will find different scripts for executing use case examples, alongside with corresponding .png images depicting their operators' and network's topology.
E.g. for running the Medical Wearables Use Case run the following commands:
python3 change_objectives.py -t 1.0 -p 0.0
The -t and -p arguments stand for the optimization weights of execution time and power consumption respectively. These weights must sum up to 1.
python3 medical_uc.py
With the above command we produce an optimal scheduling for the use case application.
If we want to change the optimization preference, in order to include power consumption optimization we simply rerun:
python3 change_objectives.py -t 0.5 -p 0.5
python3 medical_uc.py
The operators' assignment will now be different, due to the change of weights.