leafsim 0.1.2

A simulator for Large Energy-Aware Fog computing environments.

LEAF

LEAF is a simulator for Large Energy-Aware Fog computing environments. It enables then modeling of complex application graphs in distributed, heterogeneous, and resource-constrained infrastructures. A special emphasis was put on the modeling of energy consumption (and soon carbon emissions).

Please visit the official documentation for more information and examples on this project.

This Python implementation was ported from the original Java protoype. All future development will take place in this repository.

Installation

You can use LEAF either by directly cloning this repository or installing the latest release via pip:

$ pip install leafsim

What can I do with it?

LEAF enables a high-level modeling of cloud, fog and edge computing environments. It builds on top of networkx, a library for creating and manipulating complex networks, and SimPy, a process-based discrete-event simulation framework.

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 for more information and examples on this project.

Publications

The paper behind LEAF is accepted for publication:

• Philipp Wiesner and Lauritz Thamsen. "LEAF: Simulating Large Energy-Aware Fog Computing Environments" To appear in the Proceedings of the 5th IEEE International Conference on Fog and Edge Computing (ICFEC). IEEE, 2021.