GitLocker: The Coding Marketplace

Description:

pacssim 0.1.4

This is a project done in PACS Lab
aiming to develop a performance simulator for serverless computing
platforms. Using this simulator, we can calculate Quality of Service
(QoS) metrics like average response time, the average probability of
cold start, average running servers (directly reflecting average cost),
a histogram of different events, distribution of the number of servers
throughout time, and many other characteristics.
The developed performance model can be used to debug/improve analytical
performance models, try new and improved management schema, or dig up a
whole lot of properties of a common modern scale-per-request serverless
platform.

Artifacts

PyPi Package
Github Repo
ReadTheDocs
Documentation
(PDF)
Examples (MyBinder Jupyter
Lab)

Requirements

Python 3.6 or above
PIP

Installation
Install using pip:
pip install pacssim
Upgrading using pip:
pip install pacssim --upgrade
For installation in development mode:
git clone https://github.com/pacslab/pacssim
cd pacssim
pip install -e .
And in case you want to be able to execute the examples:
pip install -r examples/requirements.txt

Usage
A simple usage of the serverless simulator is shown in the following:
from pacssim.ServerlessSimulator import ServerlessSimulator as Sim

sim = Sim(arrival_rate=0.9, warm_service_rate=1/1.991, cold_service_rate=1/2.244,
expiration_threshold=600, max_time=1e6)
sim.generate_trace(debug_print=False, progress=True)
sim.print_trace_results()
Which prints an output similar to the following:
100%|██████████| 1000000/1000000 [00:42<00:00, 23410.45it/s]
Cold Starts / total requests: 1213 / 898469
Cold Start Probability: 0.0014
Rejection / total requests: 0 / 898469
Rejection Probability: 0.0000
Average Instance Life Span: 6335.1337
Average Server Count: 7.6612
Average Running Count: 1.7879
Average Idle Count: 5.8733
Using this information, you can predict the behaviour of your system in
production.

Development
In case you are interested in improving this work, you are always
welcome to open up a pull request. In case you need more details or
explanation, contact me.
To get up and running with the environment, run the following after
installing Anaconda:
conda env create -f environment.yml
conda activate simenv
pip install -r requirements.txt
pip install -e .
After updating the README.md, use the following to update the README.rst
accordingly:
bash .travis/readme_prep.sh

Examples
Some of the possible use cases of the serverless performance simulator
are shown in the examples folder in our Github repository.

License
Unless otherwise specified:
MIT (c) 2020 Nima Mahmoudi & Hamzeh Khazaei

Citation
You can find the paper with details of the simultor in PACS lab
website. You can use the
following bibtex entry for citing our work:
@software{mahmoudi_nima_2020_3906617,
author = {Mahmoudi, Nima and
Khazaei, Hamzeh},
title = {{PACSSIM: A Performance Simulator for Serverless
Computing Platforms}},
month = jun,
year = 2020,
publisher = {Zenodo},
version = {0.1.3},
doi = {10.5281/zenodo.3906617},
url = {https://doi.org/10.5281/zenodo.3906617}
}