vedic-partap/ns-3-dev-git

Network Traffic Dynamics Using Temporal Point Process

An Open Source project

Network Transmission Point Process (NTPP), a probabilistic deep machinery that models the traffic characteristics of hosts on a network and effectively forecasts the network traffic patterns, such as load spikes. Existing stochastic models relied on the network traffic being self-similar in nature, thus failing to account for traffic anomalies. These anomalies, such as short-term traffic bursts, are very prevalent in certain modern-day traffic conditions, e.g. datacenter traffic, thus refuting the assumption of self-similarity. Our model is robust to such anomalies since it effectively leverages the self-exciting nature of the bursty network traffic using a temporal point process model.

Building NTPP

The ns–3 simulator is an open-source networking simulation tool implemented by C++ and wildly used for network research and education. Currently, more and more researchers are willing to apply AI algorithms to network research. Most AI algorithms are likely to rely on open source frameworks such as TensorFlow and PyTorch. These two parts are developed independently and extremely hard to merge, so it is more reasonable and convenient to connect these two tasks with data interaction.

We have build the Deep Learning model using Pytorch in python. After training the model we learn the paramters and using those paramters we build this model in NS-3.

Currently we haven't integrated the DL model directly with ns-3 code. Although there are some projects available that can integrate them Example: hust-diangroup/ns3-ai

This module does not provide any AI algorithms or rely on any frameworks but instead is providing a Python module that enables AI interconnect, so the AI framework needs to be separately installed. You only need to clone or download this work, then import the Python modules, you could use this work to exchange data between ns-3 and your AI algorithms

Building ns-3 Model

Follow How to make and use new application?

NTPP Model Files

├── src               
│   │
│   └── applications         
│       ├── helper
|       |     │                
│       |     ├── NTPP-helper.h
│       |     └── NTPP-helper.cc
|       |
|       |
|       └── model                 
│             ├── NTPP-application.h
│             └── NTPP-application.cc
|      
└── scratch
      └── Examples
            └──NTPP-application-example.cc

Running NTPP Application

Installation

1. Install this module in ns-3

Get ns-3:

This module needs to be built within ns-3, so you need to get a ns-3-dev or other ns-3 codes first.

Check ns-3 installation wiki for detailed instructions. But insteaad of downloading the ns-3-dev-git. Install this repo.

cd ns-3-allinone/
git clone https://github.com/vedic-partap/ns-3-dev-git.git

2. Build the new application

./waf build

3. Run NTPP Application Example

./waf --run Examples

Augurnet

To see the Pytorch model for NTPP. Visit vedic-partap/augurnet

Getting access to the ns-3 documentation

Once you have verified that your build of ns-3 works by running the simple-point-to-point example as outlined in 3) above, it is quite likely that you will want to get started on reading some ns-3 documentation.

All of that documentation should always be available from the ns-3 website: http:://www.nsnam.org/documentation/.

This documentation includes:

a tutorial
a reference manual
models in the ns-3 model library
a wiki for user-contributed tips: http://www.nsnam.org/wiki/
API documentation generated using doxygen: this is a reference manual, most likely not very well suited as introductory text: http://www.nsnam.org/doxygen/index.html

Working with the development version of ns-3

If you want to download and use the development version of ns-3, you need to use the tool git. A quick and dirty cheat sheet is included in the manual, but reading through the git tutorials found in the Internet is usually a good idea if you are not familiar with it.

If you have successfully installed git, you can get a copy of the development version with the following command:

git clone https://gitlab.com/nsnam/ns-3-dev.git

However, we recommend to follow the Gitlab guidelines for starters, that includes creating a Gitlab account, forking the ns-3-dev project under the new account's name, and then cloning the forked repository. You can find more information in the manual [link].

Use for Academic puposes


@inproceedings{DBLP:conf/infocom/SahaGCD19,
  author    = {Avirup Saha and
               Niloy Ganguly and
               Sandip Chakraborty and
               Abir De},
  title     = {Learning Network Traffic Dynamics Using Temporal Point Process},
  booktitle = {2019 {IEEE} Conference on Computer Communications, {INFOCOM} 2019,
               Paris, France, April 29 - May 2, 2019},
  pages     = {1927--1935},
  publisher = {{IEEE}},
  year      = {2019},
  url       = {https://doi.org/10.1109/INFOCOM.2019.8737622},
  doi       = {10.1109/INFOCOM.2019.8737622},
  timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
  biburl    = {https://dblp.org/rec/conf/infocom/SahaGCD19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}