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https://github.com/edgerun/faas-sim

A framework for trace-driven simulation of serverless Function-as-a-Service platforms
https://github.com/edgerun/faas-sim

edge-computing python serverless-computing simpy simulation

Last synced: 3 months ago
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A framework for trace-driven simulation of serverless Function-as-a-Service platforms

Host: GitHub
URL: https://github.com/edgerun/faas-sim
Owner: edgerun
License: mit
Created: 2020-06-08T16:07:24.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2024-07-07T19:49:56.000Z (4 months ago)
Last Synced: 2024-07-07T20:55:58.435Z (4 months ago)
Topics: edge-computing, python, serverless-computing, simpy, simulation
Language: Python
Homepage: https://edgerun.github.io/faas-sim/
Size: 10.2 MB
Stars: 61
Watchers: 3
Forks: 17
Open Issues: 8
Metadata Files:
- Readme: README.md
- License: LICENSE
- Authors: AUTHORS

Awesome Lists containing this project

awesome-edge-computing - faas-sim - sim is a powerful (Uncategorized / Uncategorized)

README

        faas-sim: A trace-driven Function-as-a-Service simulator

========================================================

Faas-sim is a powerful trace-driven simulation framework to simulate container-based function-as-a-service platforms.

It can be used to develop, and evaluate the performance of operational strategies for such systems, like scheduling, autoscaling, load balancing, and others.

faas-sim was developed at the [Distributed Systems Group](https://dsg.tuwien.ac.at) at TU Wien as part of a larger research effort surrounding serverless edge computing systems.

High-level architecture

-----------------------

faas-sim is based on the [SimPy](https://simpy.readthedocs.io) discrete-event simulation framework.

It uses [Ether](https://github.com/edgerun/ether) as network simulation layer, and to create cluster configurations and network topologies.

By default, it uses the [Skippy](https://github.com/edgerun/skippy-core) scheduling system for resource scheduling,

but schedulers, autoscalers, and load-balancers can be plugged in by the user.

faas-sim is trace-driven, and relies on profiling data from workloads and devices to simulate function execution.

It comes pre-packaged with traces from several common computing devices and representative cluster workloads.

The following figure shows a high-level overview:



Run examples

------------

You can run the examples we provide in https://github.com/edgerun/faas-sim/tree/master/examples by first creating a virtual environment and installing the necessary dependencies.

    make venv

    source .venv/bin/activate

    python -m examples..main

Where example refers to the specific example package.

Check out the examples [README](https://github.com/edgerun/faas-sim/tree/master/examples/README.md) for more information.

Run notebooks

-------------

Notebooks are located in `notebooks`.

You need to install `faas-sim` in editable state to run the notebooks.

Inside `notebooks` import modules from `sim`.

To install the project (assuming you already created and activated a virtual environment via `make venv`):

      pip install -e .

      jupyter notebook

Documentation

-------------

You can find the documentation at https://edgerun.github.io/faas-sim/

Maintainers

------------

* [Thomas Rausch](https://github.com/thrau)

* [Philipp Raith](https://github.com/phip123)

Development

-----------

The simulator has seen a major refactoring in the branch `/feature/adapt-to-galileo-faas` and aims to be compatible with the other galileo projects.

Only this branch is in active development.

Related publications

--------------------

1. Raith, P., Rausch, T., Furutanpey, A., & Dustdar, S. (2023).

   **faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms.**

   In *Software: Practice and Experience*. Wiley Online Library.

   [[Paper](https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.3277)]

1. Raith, P. (2021)

   Container Scheduling on Heterogeneous Clusters using Machine Learning-based Workload Characterization.

   *Diploma Thesis*. TU Wien.

   [[Thesis](https://repositum.tuwien.at/handle/20.500.12708/16871)]

1. Rausch, T., Lachner, C., Frangoudis, P. A., Raith, P., & Dustdar, S. (2020).

   Synthesizing Plausible Infrastructure Configurations for Evaluating Edge Computing Systems.

   In *3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 20)*. USENIX Association.

   [[Paper](https://www.usenix.org/conference/hotedge20/presentation/rausch)]

1. Rausch, T., Rashed, A., & Dustdar, S. (2020)

   Optimized container scheduling for data-intensive serverless edge computing.

   In *Future Generation Computer Systems.*.

   [[Paper](https://www.sciencedirect.com/science/article/pii/S0167739X2030399X)]

1. Rashed, A. (2020)

   Optimized Container Scheduling for Serverless Edge Computing.

   *Diploma Thesis*. TU Wien.

   [[Thesis](http://repositum.tuwien.ac.at/obvutwhs/content/titleinfo/4671607)]