RayCloudSim: A Simulation Platform Written in Python for Cloud/Fog/Edge Computing

I. Update Summary

Only the summaries of the most recent three updates will be recorded here. The complete history of all update summaries can be viewed here.

• 2025/02/04

  • [New] New dataset: Pakistan
  • [New] Adding support for distance calculation based on the Haversine formula
  • [New] New offloading policies: Round Robin, Greedy and DQRL

• 2024/07/02

  • [New] New dataset: Topo4MEC

• 2024/04/26

  • [New] Users can now easily create the Scenario using configuration files in JSON format
  • [Optimization] The dataset has been optimized for saving in CSV format, offering better readability
  • [New/Optimization] More and better visualization tools, including simulation processes reproduced in video format, facilitate an intuitive understanding of the simulation process

II. Contributing

Any contributions you make are greatly appreciated. Please note the following:

1. Please note that the main branch is protected, and we recommend that you submit pull request to the dev-open branch.
2. All scripts under the examples/ directory have corresponding outputs, and these records also serve as a proof of code execution. Please ensure that your pull request does not change the corresponding output records (or provide reasonable explanations).

Join and build RayCloudSim together

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III. Introduction

RayCloudSim is a lightweight simulator written in Python for analytical modeling and simulation of Cloud/Fog/Edge Computing infrastructures and services. The original intention for the development of RayCloudSim was for research related to task offloading, and it now supports a more diverse range of research topics.

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:

• Research on task offloading in cloud/fog/edge computing
• Research on performance and cost analysis of cloud/fog/edge computing
• Research on traffic analysis of complex networks
• Research on resource management and scheduling strategies for large-scale distributed systems
• Research on deployment strategies for specific devices, such as parameter servers in federated learning
• ...

IV. Requirements & Installation

Main Dependent Modules:

• python >= 3.8: Previous versions might be OK but without testing.
• networkx: NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
• simpy: SimPy is a process-based discrete-event simulation framework based on standard Python.
• numpy: NumPy is a Python library used for working with arrays.
• pandas: Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool.

The following modules are used for visualization tools:

• matplotlib
• cv2
• tensorboard

Users are recommended to use the Anaconda to configure the RayCloudSim:

conda create --name raycloudsim python=3.8
conda activate raycloudsim
pip install -r requirements.txt

V. Set Sail

1. Hello World

# Create the Env
scenario=Scenario(config_file="examples/scenarios/configs/config_1.json")
env = Env(scenario, config_file="core/configs/env_config.json")

# Begin Simulation
task = Task(task_id=0,
            task_size=20,
            cycles_per_bit=10,
            trans_bit_rate=20,
            src_name='n0')

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

env.run(until=20)

print("\n-----------------------------------------------")
print("Energy consumption during simulation:\n")
print(f"n0: {env.node_energy('n0'):.3f}")
print(f"n1: {env.node_energy('n1'):.3f}")
print(f"Averaged: {env.avg_node_energy():.3f}")
print("-----------------------------------------------\n")

env.close()

Simulation log:

[0.00]: Task {0} generated in Node {n0}
[0.00]: Task {0}: {n0} --> {n1}
[1.00]: Task {0} arrived Node {n1} with {1.00}s
[1.00]: Processing Task {0} in {n1}
[11.00]: Task {0} accomplished in Node {n1} with {10.00}s

-----------------------------------------------
Energy consumption during simulation:

n0: 0.000
n1: 0.072
Averaged: 0.036
-----------------------------------------------

[20.00]: Simulation completed!

2. Tutorials

(1). The following figure presents the framework of RayCloudSim, which consists of two main components：Env and Task:

(2). A Simple Introduction to System Modeling: docs/RayCloudSim.md

(3). The following scripts can be used as progressive tutorials.

• examples/*

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

(4). RayCloudSim supports multiple visualization features: static visualization of system topology, dynamic visualization of the simulation process, etc.

• static visualization of system topology

• dynamic visualization of the simulation process

The complete video:

• Github

• Baidu Netdisk (Access code: xa1r)

VI. 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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