RayCloudSim: A Simulator Written in Python for Cloud, Fog, or Edge Computing

I. Introduction

RayCloudSim is a simulator written in Python for analytical modeling in cloud, fog, or edge computing environments.

RayCloudSim is a lightweight simulator written in Python for analytical modeling and simulation of Cloud/Fog/Edge Computing infrastructures and services.

RayCloudSim has the following advantages:

• Compact source code, which is easy to read, understand and customize according to individual needs.
• It is a process-based discrete-event simulation framework and can be performed "as fast as possible", in wall clock time.
• It is easy to integrate with machine learning frameworks such as PyTorch, TensorFlow, and other Python-based ML frameworks.

RayCloudSim can be used for the following research topics:

• Performance and cost analysis of cloud computing and edge computing
• Traffic analysis of complex networks
• Research on resource management and scheduling strategies for large-scale distributed systems
• Research on task allocation and scheduling algorithms
• Research on deployment of specific devices, such as parameter servers in federated learning.
• ...

II. Requirements & Installation

• python >= 3.8

  previous version might be OK but without testing.

• numpy

• networkx

• simpy

The following packages are optional for visualization.

• matplotlib
• plotly
• kaleido

Commands for configuring the RayCloudSim using Anaconda:

conda create --name raycloudsim python=3.8
conda activate raycloudsim
conda install -c anaconda numpy
conda install -c conda-forge matplotlib
conda install -c anaconda networkx
conda install -c conda-forge simpy
conda install -c plotly plotly
conda install -c conda-forge python-kaleido

III. Set Sail

1. Hello World

# Create the Env
env = Env(scenario=Scenario())

# Begin Simulation
task = Task(task_id=0,
            max_cu=10,
            task_size_exe=20,
            task_size_trans=10,
            bit_rate=20,
            src_name='n0')

env.process(task=task, dst_name='n1')

env.run(until=10)

env.close()

Simulation log:

[0.00]: Task {0} generated in Node {n0}
[4.00]: Task {0} arrived Node {n1} with {4.00}s
[6.00]: Task {0} accomplished in Node {n1} with {2.00}s
[10.00]: Simulation completed!

2. Guides

• The following figure presents the framework of RayCloudSim, which consists of two main components：Env and Task:

• Note that learning how to use Simpy would be very helpful.

• docs/RayCloudSim.md

3. Tutorials

The following scripts can be used as progressive tutorials.

• examples/demo1.py

• examples/demo2.py

• examples/demo3.py

• examples/demo4.py

The following figure illustrates a visualization example:

IV. Development Plan

1. TODO

• The basic version. (2023/05/10)
• Module Zoo, such as WirelessNode, etc. (2023/10/24)
• Add buffer in computing nodes for task queue. (2023/11/10)
• Support using wireless nodes as relay communication nodes?
• Modeling of energy consumption.
• Package and publish to PyPI.

2. Contribute Code to RayCloudSim

We welcome any contributions to the codebase. The branch main is protected, and we recommend that you submit/push code to branch dev.

Citation

To cite this repository, you can use the following BibTeX entry:

@article{zhang2022osttd,
  title={OSTTD: Offloading of Splittable Tasks with Topological Dependence in Multi-Tier Computing Networks},
  author={Zhang, Rui and Chu, Xuesen and Ma, Ruhui and Zhang, Meng and Lin, Liwei and Gao, Honghao and Guan, Haibing},
  journal={IEEE Journal on Selected Areas in Communications},
  year={2022},
  publisher={IEEE}
}

Besides, RayCloudSim is inspired by LEAF and the following citation is also recommended.

@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}
}

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