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https://github.com/dos-group/leaf

Simulator for modeling energy consumption in cloud, fog, and edge computing environments 🌱
https://github.com/dos-group/leaf

edge-computing energy-consumption fog-computing modeling simulation

Last synced: about 2 months ago
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Simulator for modeling energy consumption in cloud, fog, and edge computing environments 🌱

Host: GitHub
URL: https://github.com/dos-group/leaf
Owner: dos-group
License: mit
Created: 2021-01-22T17:00:06.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-04-02T07:53:34.000Z (6 months ago)
Last Synced: 2024-06-11T17:26:57.832Z (3 months ago)
Topics: edge-computing, energy-consumption, fog-computing, modeling, simulation
Language: Python
Homepage: https://leaf.readthedocs.io
Size: 1.16 MB
Stars: 98
Watchers: 14
Forks: 13
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

open-sustainable-technology - LEAF - Simulator for modeling energy consumption in cloud, fog, and edge computing environments. (Consumption / Computation and Communication)

README

        # LEAF [![PyPI version](https://img.shields.io/pypi/v/leafsim.svg?color=52c72b)](https://pypi.org/project/leafsim/) [![Supported versions](https://img.shields.io/pypi/pyversions/leafsim.svg)](https://pypi.org/project/leafsim/) [![License](https://img.shields.io/pypi/l/leafsim.svg)](https://pypi.org/project/leafsim/)



LEAF is a simulator for analytical modeling of energy consumption in cloud, fog, or edge computing environments.

It enables the modeling of simple tasks running on a single node as well as complex application graphs in distributed, heterogeneous, and resource-constrained infrastructures.

LEAF is based on [SimPy](https://simpy.readthedocs.io/en/latest/) for discrete-event simulation and [NetworkX](https://networkx.org/) for modeling infrastructure or application graphs.

Please have a look at out [examples](https://github.com/dos-group/leaf/tree/main/examples) and visit the official [documentation](https://leaf.readthedocs.io) for more information on this project.

This Python implementation was ported from the [original Java protoype](https://www.github.com/birnbaum/leaf).

All future development will take place in this repository.

## ⚙️ Installation

You can install the [latest release](https://pypi.org/project/leafsim/) of LEAF via [pip](https://pip.pypa.io/en/stable/quickstart/):

```

$ pip install leafsim

```

Alternatively, you can also clone the repository (including all examples) and set up your environment via:

```

$ pip install -e .

```

## 🚀 Getting started

LEAF uses [SimPy](https://simpy.readthedocs.io/en/latest/) for process-based discrete-event simulation and adheres to their API.

To understand how to develop scenarios in LEAF, it makes sense to familiarize yourself with SimPy first.

```python

import simpy

from leaf.application import Task

from leaf.infrastructure import Node

from leaf.power import PowerModelNode, PowerMeter

# Processes modify the model during the simulation

def place_task_after_2_seconds(env, node, task):

    """Waits for 2 seconds and places a task on a node."""

    yield env.timeout(2)

    task.allocate(node)

node = Node("node1", cu=100, power_model=PowerModelNode(max_power=30, static_power=10))

task = Task(cu=100)

power_meter = PowerMeter(node, callback=lambda m: print(f"{env.now}: Node consumes {int(m)}W"))

env = simpy.Environment()  

# register our task placement process

env.process(place_task_after_2_seconds(env, node, task))

# register power metering process (provided by LEAF)

env.process(power_meter.run(env))

env.run(until=5)

```

Which will result in the output:

```

0: Node consumes 10W

1: Node consumes 10W

2: Node consumes 30W

3: Node consumes 30W

4: Node consumes 30W

```

For other examples, please refer to the [examples folder](https://github.com/dos-group/leaf/blob/main/examples).

## 🍃 What can I do with LEAF?

LEAF enables high-level simulation of computing scenarios, where experiments are easy to create and easy to analyze.

Besides allowing research on scheduling and placement algorithms on resource-constrained environments, LEAF puts a special focus on:

- **Dynamic networks**: Simulate mobile nodes which can join or leave the network during the simulation.

- **Power consumption 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.

- **Scalability**: Model the execution of thousands of compute nodes and applications in magnitudes faster than real time.

Please visit the official [documentation](https://leaf.readthedocs.io) for more information and examples on this project.



  



## 📖 Publications

If you use LEAF in your research, please cite our paper:

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)

Bibtex:

```

@inproceedings{WiesnerThamsen_LEAF_2021,

  author={Wiesner, Philipp and Thamsen, Lauritz},

  booktitle={2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)}, 

  title={{LEAF}: Simulating Large Energy-Aware Fog Computing Environments}, 

  year={2021},

  pages={29-36},

  doi={10.1109/ICFEC51620.2021.00012}

}

```

## 💚 Projects using LEAF

- Shivani Tripathi, Praveen Kumar, Priyadarshni Gupta, Rajiv Misra, and T.N. Singh. "[Workload Shifting Based on Low Carbon Intensity Periods: A Framework for Reducing Carbon Emissions in Cloud Computing]()". *2023 IEEE International Conference on Big Data (BigData)*. IEEE. 2023.

- Rui Zhang, Xuesen Chu, Ruhui Ma, Meng Zhang, Liwei Lin, Honghao Gao, and Haibing Guan. "[OSTTD: Offloading of Splittable Tasks with Topological Dependence in Multi-Tier Computing Networks](https://ieeexplore.ieee.org/abstract/document/9978666)". *IEEE Journal on Selected Areas in Communications*. 2022.

- Zizheng Liu. "A Web-Based User Interface for the LEAF Simulator". *MSc IT+ Dissertation* at U of Glasgow. 2022 [[code](https://github.com/ZZZZZZZZZED/leaf-GUI)]

- Yana Kernerman. "Interactive Visualization of Energy Consumption in Edge and Fog Computing Simulations". *Bachelor Thesis* at TU Berlin. 2022 [[code](https://github.com/dos-group/leaf/tree/gui)]

- Sangeeta Kakati and Rupa Deka. "[Computational and Adaptive Offloading in Edge/Fog based IoT Environments](https://ieeexplore.ieee.org/document/9847743)." *2nd International Conference on Intelligent Technologies (CONIT)*. IEEE. 2022

- Philipp Wiesner, Ilja Behnke, Dominik Scheinert,  Kordian Gontarska, and Lauritz Thamsen. "[Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud](https://arxiv.org/pdf/2110.13234.pdf)". *22nd International Middleware Conference*. ACM. 2021 [[code](https://github.com/dos-group/lets-wait-awhile)]

- Liam Brugger. "An Evaluation of Carbon-Aware Load Shifting Techniques". *Bachelor Thesis* at TU Berlin. 2021 [[code](https://gitlab.com/lbrugger72/Bachelor)]