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README.md

YAFS logo

NEWS

A new version of YAFS is available in the branch.

  • It supports +Python3.6.
  • It depends on fewer third libraries. It is lighter and easier to install.
  • It has 4 awesome "tutorial_scenarios" or skeletons so you can use them to create your scenario with artificial intelligence, rules, neural networks, ... with whatever you want.
  • Notes:
    • Previous examples in folder "examples" are not up to date for this version yet, but the code is kept for you to inspire.
    • Some parts of the Doc are not still updated.

DESCRIPTION

YAFS (Yet Another Fog Simulator) is a simulator tool based on Python of architectures such as: Fog Computing ecosystems for several analysis regarding with the placement of resources, cost deployment, network design, ... IoT environments are the most evident fact of this type of architecture.

The highlights points of YAFS are:

  • Dinamyc topology: entities and network links can be created or removed along the simulation.
  • Dinamyc creation of messages sources: sensors can generate messages from different point access along the simulation.
  • And for hence, the placement allocation algorithm and the orchestration algorithm, that are extended by the user, can run along the simulation.
  • The topology of the network is based on Complex Network theory. Thus, the algorithms can obtain more valuable indicators from topological features.
  • The results are stored in a raw format in a nosql database. The simpler the format, the easier it is to perform any type of statistics.

YAFS is released under the MIT License. However, we would like to know in which project or publication have you used or mentioned YAFS.

Please consider use this cite when you use YAFS:

    Isaac Lera, Carlos Guerrero, Carlos Juiz. YAFS: A simulator for IoT scenarios in fog computing. IEEE Access. Vol. 7(1), pages 91745-91758,
    10.1109/ACCESS.2019.2927895, Jul 10 2019.

    https://ieeexplore.ieee.org/document/8758823

    @ARTICLE{8758823,
    author={I. {Lera} and C. {Guerrero} and C. {Juiz}},
    journal={IEEE Access},
    title={YAFS: A Simulator for IoT Scenarios in Fog Computing},
    year={2019},
    volume={7},
    number={},
    pages={91745-91758},
    keywords={Relays;Large scale integration;Wireless communication;OFDM;Interference cancellation;Channel estimation;Real-time systems;Complex networks;fog computing;Internet of Things;simulator},
    doi={10.1109/ACCESS.2019.2927895},
    ISSN={2169-3536},
    month={},
    }

Resources

YAFS tutorial (https://yafs.readthedocs.io/en/latest/introduction/index.html) and user guide (https://www.slideshare.net/wisaaco/yet-another-fog-simulator-yafs-user-guide) are a good starting point for you. You can also try out some of the Examples (https://yafs.readthedocs.io/en/latest/examples/index.html) shipped with YAFS but in any case you have to understand the main concepts of Cloud Computing and other related architectures to design and modelling your own model.

Installation (updated)

YAFS requires Python +3.6 (Python 3.6 or above is not supported)

  1. Clone the project in your local folder:
    $ git clone --branch YAFS3 https://github.com/acsicuib/YAFS
  1. Create one python virtual environment and install dependencies
   (venv)$ python -m pip install -r requirements.txt
  1. I recommend you IDE such as Pycharm (educational licence) to configure Python paths.

Getting started & your first execution

To run some folder project you can create a simple bash script, with the following lines (please update the path according with your system) or you can use a python editor such as: Pycharm, Spyder, etc. Personally, I recommend you Pycharm since it integrates all the paths.

export PYTHONPATH=$PYTHONPATH:/<your path>/YAFS/src/:src/examples/Tutorial/
python src/examples/Tutorial/main1.py

The YAFS tutorial is a good starting point for you. You can also try out some of the Examples shipped with YAFS but in any case you have to understand the main concepts of Cloud Computing and other related architectures to design and modelling your own model.

Graph animations

As you can implement events (custom strategies), you can generate plots of your network in each event. Thus, you can store png files and at the end of your simulation, you generate a video with the combination all of them using ffmpeg command.

You can find some examples in the following src/examples: DynamicWorkloads, ConquestService, and mobileTutorial. From DynamicWorkload folder and ConquestService, we have generate the following animations:

ffmpeg -r 1 -i net_%03d.png -c:v libx264 -vf fps=1 -pix_fmt yuv420p out.mp4
ffmpeg -i out.mp4 -pix_fmt rgb24  out.gif

Documentation and Help

The documentation contains a tutorial, the architecture design explaining key concepts, a number of examples and the API reference.

For more help, contact with the authors or You must dig through the source code

Improvements

  • sep. / 12 / 2019 Fixing bugs - All projects work with the attributes defined in the graph var (topology class) using NX library to manage the attributes.
  • may / 23 / 2019 New improvements are included. Highlight that workloads/users and mobile endpoints can be represented through gpx traces. Geopositional libraries are required
  • june / 25 / 2018 Bug Fixed - The DES.src metric of the CSV results is fixed. Identifies the DES-process who sends the message
  • june / 20 / 2018 Messages from sources have an unique identifier that is copied in all the transmissions. We can trace each application invocation.

Acknowledgment

Authors acknowledge financial support through grant project ORDCOT with number TIN2017-88547-P (AEI/FEDER, UE)

REFERENCES

YAFS is used in the following projects:

  • Isaac Lera, Carlos Guerrero, Carlos Juiz. YAFS: A simulator for IoT scenarios in fog computing. IEEE Access. Vol. 7(1), pages 91745-91758, 10.1109/ACCESS.2019.2927895, Jul 10 2019.
  • Isaac Lera, Carlos Guerrero, Carlos Juiz. Comparing centrality indices for network usage optimization of data placement policies in fog devices. FMEC 2018: 115-122
  • Carlos Guerrero, Isaac Lera, Carlos Juiz. Migration-Aware Genetic Optimization for MapReduce Scheduling and Replica Placement in Hadoop. Journal of Grid Computing 2018. 10.1007/s10723-018-9432-8
  • Isaac Lera, Carlos Guerrero, Carlos Juiz. Availability-aware Service Placement Policy in Fog Computing Based on Graph Partitions. IEEE Internet of Things Journal 2019. 10.1109/JIOT.2018.2889511
  • Isaac Lera, Carlos Guerrero, Carlos Juiz. Analysing the Applicability of a Multi-Criteria Decision Method in Fog Computing Placement Problem. FMEC 2019

Please, send us your reference to publish it!