Ethereum Consensus Clients Hardware Analysis V3

This repository collects all the scripts and generates the figures used in the Ethereum Consensus Client's hardware resource analysis.

The Analysis

This Analysis was performed during the months of February 2024 and May 2024. It consisted of running the 6 main clients (Prysm, Lighthouse, Teku, Nimbus, Lodestar and Grandine)

Running the clients

Inside this repository you may find several files with which you launch the nodes you intend to analyze. The setup includes an execution node (always Nethermind), a consensus node (Lighthouse, Prysm, Teku, Nimbus, Lodestar, Grandine) and some monitoring tools (such as Prometheus, Node Exporter or CAdvisor)

Server

It is possible to run a metrics server, which should aborb all the generated metrics into a single prometheus instance. This is very useful when the experiment involves several clients and machines. This way, analyzing the data is faster and easier, as everything is contained in a single instance.

Caddy is a Proxy server which will receive all the incoming traffic and redirect it to the metrics server.

1. Copy Caddyfile.sample into Caddyfile
2. You may configure some credentials if needed.
3. Copy prometheus-template.yml into prometheus.yml (prometheus folder). You may comment all the scrapes as this prometheus will be a server receiving the data.
4. Run docker-compose up -d prometheus victoriametrics caddy
5. You may now use http://user:password@yourIP/promhttp/api/v1/write and http://user:password@yourIP/victoria/api/v1/write as remote write

Clients

1. Decide which client to run
2. Copy .env.sample into .env and fill the variables. Tags refer to the client version (docker image).
3. Copy prometheus-template.yml into prometheus.yml (prometheus folder). See remote write above if needed, comment it otherwise.
4. You may configure which clients to scrape and the remote write (see server options above).
5. Run docker-compose up -d nethermind <cl-node> prometheus cadvisor node-exporter.

Data Collection

Data has been collected using a Jupyter Notebook and a Prometheus. Please refer to the report.ipynb file, where you may find a page containing the data collection details. After the execution, you may find several CSV files have been downloaded (under {network}/csv/).

Data Plotting

Data has been plotted using the same Jupyter Notebook report.ipynb. The last page contains details about the plots. These plots are built from the CSV files downloaded previously with the same document.

Generating metrics

First of all we must place ourselves in the analysis folder

It is important to load the requirements into a venv

Most of the figures can be generated by running download_csvs.py and then running plot.py

In those files, one must edit the main() to select which plots and csvs to generate.

Before all, the base.py contains a section called run1 and another run2. These define the times at which to download and plot the data, and which files to use. By commenting and uncommenting run2 section, we can select which phase to download and plot. By default, we have commented run1

Maintained by MigaLabs