rusyadiramli/YAFS

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:

    PENDING

Installation

YAFS requires Python 2.7 (Python 3.6 or above is not supported)

You can download and install YAFS manually:

    $ git clone https://github.com/acsicuib/YAFS

To install third-libraries you can execute:

    python setup.py install

Dependencies are:

Simpy
Networkx
Numpy
Pandas
tqdm

Getting started

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.

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.

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

A "SUPER" TIP

We try to implement a wonderful tutorial but our time is limited. Thus, we can introduce this simple tip to create a custom strategy in the simulation.

For example, in your main.py function, you can declare a custom strategy with a deterministic distribution, and you can include the parameters that you want, i.e. the simulator class, and the routingPath.

dStart = deterministicDistributionStartPoint(400, 100, name="Deterministic")
evol= CustomStrategy()
s.deploy_monitor("EvolutionOfServices",evol,dStart,**{"sim":s,"routing":selectorPath})

And finally you define the CustomStrategy() class:

class CustomStrategy():

    def __call__(self, sim,routing):
        sim.print_debug_assignaments()
        routing.print_control_services()
        routing.my_var = False
        #or whatever you want

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

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. Comparing centrality indices for network usage optimization of data placement policies in fog devices. FMEC 2018: 115-122

Please, send us your reference to publish it!

rusyadiramli / YAFS