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and Communication","monthly_downloads":38,"total_dependent_repos":2,"total_dependent_packages":0,"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/)\n\n\u003cimg align=\"right\" width=\"300\" height=\"230\" src=\"https://leaf.readthedocs.io/en/latest/_static/logo.svg\"\u003e\n\nLEAF is a simulator for analytical modeling of energy consumption in cloud, fog, or edge computing environments.\nIt enables the modeling of simple tasks running on a single node as well as complex application graphs in distributed, heterogeneous, and resource-constrained infrastructures.\nLEAF 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.\n\nPlease 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.\n\nThis Python implementation was ported from the [original Java protoype](https://www.github.com/birnbaum/leaf).\nAll future development will take place in this repository.\n\n\n## ⚙️ Installation\n\nYou can install the [latest release](https://pypi.org/project/leafsim/) of LEAF via [pip](https://pip.pypa.io/en/stable/quickstart/):\n\n```\n$ pip install leafsim\n```\n\nAlternatively, you can also clone the repository (including all examples) and set up your environment via:\n\n```\n$ pip install -e .\n```\n\n\n## 🚀 Getting started\n\nLEAF uses [SimPy](https://simpy.readthedocs.io/en/latest/) for process-based discrete-event simulation and adheres to their API.\nTo understand how to develop scenarios in LEAF, it makes sense to familiarize yourself with SimPy first.\n\n```python\nimport simpy\nfrom leaf.application import Task\nfrom leaf.infrastructure import Node\nfrom leaf.power import PowerModelNode, PowerMeter\n\n# Processes modify the model during the simulation\ndef place_task_after_2_seconds(env, node, task):\n    \"\"\"Waits for 2 seconds and places a task on a node.\"\"\"\n    yield env.timeout(2)\n    task.allocate(node)\n\nnode = Node(\"node1\", cu=100, power_model=PowerModelNode(max_power=30, static_power=10))\ntask = Task(cu=100)\npower_meter = PowerMeter(node, callback=lambda m: print(f\"{env.now}: Node consumes {int(m)}W\"))\n\nenv = simpy.Environment()  \n# register our task placement process\nenv.process(place_task_after_2_seconds(env, node, task))\n# register power metering process (provided by LEAF)\nenv.process(power_meter.run(env))\nenv.run(until=5)\n```\n\nWhich will result in the output:\n\n```\n0: Node consumes 10W\n1: Node consumes 10W\n2: Node consumes 30W\n3: Node consumes 30W\n4: Node consumes 30W\n```\n\nFor other examples, please refer to the [examples folder](https://github.com/dos-group/leaf/blob/main/examples).\n\n\n## 🍃 What can I do with LEAF?\n\nLEAF enables high-level simulation of computing scenarios, where experiments are easy to create and easy to analyze.\nBesides allowing research on scheduling and placement algorithms on resource-constrained environments, LEAF puts a special focus on:\n\n- **Dynamic networks**: Simulate mobile nodes which can join or leave the network during the simulation.\n- **Power consumption modeling**: Model the power usage of individual compute nodes, network traffic, and applications.\n- **Energy-aware algorithms**: Implement dynamically adapting task placement strategies, routing policies, and other energy-saving mechanisms.\n- **Scalability**: Model the execution of thousands of compute nodes and applications in magnitudes faster than real time.\n\nPlease visit the official [documentation](https://leaf.readthedocs.io) for more information and examples on this project.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"/docs/_static/infrastructure.png\"\u003e\n\u003c/p\u003e\n\n\n## 📖 Publications\n\nIf you use LEAF in your research, please cite our paper:\n\nPhilipp 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)\n\nBibtex:\n```\n@inproceedings{WiesnerThamsen_LEAF_2021,\n  author={Wiesner, Philipp and Thamsen, Lauritz},\n  booktitle={2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC)}, \n  title={{LEAF}: Simulating Large Energy-Aware Fog Computing Environments}, \n  year={2021},\n  pages={29-36},\n  doi={10.1109/ICFEC51620.2021.00012}\n}\n```\n\n## 💚 Projects using LEAF\n\n- 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.\n- 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.\n- 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)]\n- 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)]\n- 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\n- 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)]\n- Liam Brugger. \"An Evaluation of Carbon-Aware Load Shifting Techniques\". *Bachelor Thesis* at TU Berlin. 2021 [[code](https://gitlab.com/lbrugger72/Bachelor)]\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/20182","html_url":"https://ost.ecosyste.ms/projects/20182"}