last version of README

README.md

 # Characterization of different user behaviors for demand response in data centers
-This repository contains the scripts and files needed to reproduce the experiments presented in the paper "Characterization of different user behaviors for demand response in data centers" submitted to [Euro-Par conference 2022](https://2022.euro-par.org/).
+This repository is the artifact associated to the publication "Characterization of different user behaviors for demand response in data centers" presented at [Euro-Par 2022](https://2022.euro-par.org/). It contains all the scripts and files needed to reproduce the experiments presented in the paper.
 
 ## Description of the main files
 - `prepare_workload.ipynb`: Jupyter notebook downloading and preparing the workload trace used in the experiments
-- `campaign.py`: Python script preparing and launching in parallel the 105 experiments. Each experiment corresponds to one instance of `instance.py`.
-- `analyse_campaign.ipynb`: Jupyter notebook analysing the results and plotting the graphs shown in the article
+- `campaign.py`: Python script preparing and launching in parallel the 105 experiments. Each experiment corresponds to one instance of `instance.py`
+- `analyse_campaign.ipynb`: Jupyter notebook analysing the results from the campaign and plotting the graphs shown in the article
+
+We also made use of some handy scripts to install the environment and launch the experiments on Grid'5000 that are not directly necessary to reproduce the results but that we leave in the folder `scripts` for interested readers.
 
-(campaign1 is another set of experiements not discussed in the article. We kept it here but beware that some path might be broken...)
 
 ## Steps to reproduce
+You should be able to reproduce the experiments with a standard Linux machine (laptop, desktop, one cluster node), with a graphical interface for the Jupyter notebooks. We personnaly used a 16-core Intel Xeon E5-2630 v3 machine from [Grid'5000 testbed](https://www.grid5000.fr) on which the experiments ran in less than two hours.
+
 You will need ~10GB disk space for the inputs, outputs and dependencies.
 
 ### 1. Install
@@ -23,26 +26,27 @@ The main software used (and configured in the file `default.nix`) are:
 - [Batmen](https://gitlab.irit.fr/sepia-pub/mael/batmen): our set of schedulers for batsim and plugin to simulate users
 - python3, pandas, jupyter, matplotlib etc. for the data analysis
 
-Enter a shell with all dependencies managed. This will take some time to download and compile the first time you launch it, but then all the environment is cached for future use.
+Enter a shell with all dependencies managed. This will take some time (~5mn) to download and compile averything the first time you launch it, but then all the environment is cached for future use.
 ```bash
 nix-shell -A exp_env --pure
 ```
 
 ### 2. Prepare input workload
-Inside the nix shell, start a notebook and follow the steps presented in `0_prepare_workload.ipynb`:
+Inside the nix shell, start a notebook and follow the steps presented in `prepare_workload.ipynb` (~5mn):
 ```bash
 jupyter notebook 0_prepare_workload.ipynb
 ```
 
 ### 3. Launch the campaign
-Still inside the nix shell, launch the python script. It took about 2 hours for us to execute in parallel on a 16-core Intel Xeon E5-2630 v3 machine.
+Still inside the nix shell, launch the python script (it took around 2 hours for us to execute in parallel on our 16-core machine):
 ```bash
-python3 campaign2.py
+python3 campaign.py
 ```
 
 ### 4. Analyse the results
+Finally and still in the nix shell, run this Jupyter notebook to plot the graphs displayed in the article:
 ```bash
-jupyter notebook analyse_campaign2.ipynb
+jupyter notebook analyse_campaign.ipynb
 ```
 
 ## More information about these experiments