Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/dos-group/leaf-java
A simulator for large energy-aware fog computing environments 🌱
https://github.com/dos-group/leaf-java
edge-computing energy-consumption fog-computing modeling simulation
Last synced: 3 months ago
JSON representation
A simulator for large energy-aware fog computing environments 🌱
- Host: GitHub
- URL: https://github.com/dos-group/leaf-java
- Owner: dos-group
- Created: 2020-12-04T11:40:30.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2021-07-08T14:59:53.000Z (over 3 years ago)
- Last Synced: 2024-06-11T17:26:58.189Z (5 months ago)
- Topics: edge-computing, energy-consumption, fog-computing, modeling, simulation
- Language: Java
- Homepage:
- Size: 7.15 MB
- Stars: 10
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-edge-computing - LEAF
README
# LEAF 🌱
A simulator for **L**arge **E**nergy-**A**ware **F**og computing environments.
LEAF enables energy consumption modeling of distributed, heterogeneous, and resource-constrained infrastructure that executes complex application graphs.
Features include:
- **Power modeling**: Model the power usage of individual compute nodes, network traffic and applications
- **Energy-aware algorithms**: Implement dynamically adapting task placement strategies, routing policies, and other energy-saving mechanisms
- **Dynamic networks**: Nodes can be mobile and can join or leave the network during the simulation
- **Scalability**: Simulate thousands of devices and applications in magnitudes faster than real time
- **Exporting**: Export power usage characteristics and other results as CSV files for further analysis
## Python Implementation
We created a new, lightweight implementation of LEAF in Python.
Although the Python version is currently less performant, we hope the cleaner interface, improved usability,
and bigger third party library support - especially for machine learning - will lead to a greater adoption of our model.
You can find the new implementation [here](https://github.com/dos-group/leaf) and its documentation [here](https://leaf.readthedocs.io/en/latest/).
## Core
The package `org.leaf` contains the infrastructure and application model as well as related power models.
The current implementation is based on [CloudSim Plus](https://github.com/manoelcampos/cloudsim-plus),
a modern and fully documented simulation framework.
Several features and improvements developed within LEAF were directly contributed to the CloudSim Plus core and
released in [version 6](https://github.com/manoelcampos/cloudsim-plus/releases/tag/v6.0.0).
Infrastructure and application graphs are implemented through [JGraphT](https://jgrapht.org/).
### Example Scenario
The package `org.examples.smart_city_traffic` implements an exemplary traffic management scenario in a smart city.
The example contains:
- multiple custom compute nodes, network links, and applications
- a mobility model for taxis modelled after a taxi traffic dataset from the
[2015 DEBS Grand Challenge competition](http://www.debs2015.org/call-grand-challenge.html)
- two different application placement algorithms
- a **live visualization** to monitor experiments at runtime which contains
- a map of the city and the location of taxis
- the number of taxis on the map over time
- the power usage of infrastructure components over time
- the power usage of applications over time
Experiments can be configured via the `Settings` class.
To improve simulation speed increase the `LOG_LEVEL` and reduce the `VISUALIZATION_REDRAW_INTERVAL`.
### Analysis
The directory `analysis` contains the experiment analysis code written in Python.
For running the analysis yourself, [install conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) adapt `settings.py` and run:
```
cd analysis
conda env create
conda activate leaf_analysis
python create_plots.py
```
## Publications
- Philipp Wiesner and Lauritz Thamsen. "[LEAF: Simulating Large Energy-Aware Fog Computing Environments](https://ieeexplore.ieee.org/document/9458907)" In the Proceedings of the 2021 *5th IEEE International Conference on Fog and Edge Computing (ICFEC)*, IEEE, 2021. [[arXiv preprint]](https://arxiv.org/pdf/2103.01170.pdf) [[video]](https://youtu.be/G70hudAhd5M)