Open Source Linux Network Simulators

Build, Design and Test your network in a risk-free virtual environment and access the largest networking community to help. Whether you are studying for your first networking exam or building out a state-wide telecommunications network, GNS3 offers an easy way to design and build networks of any size without the need for hardware.

The Common Open Research Emulator (CORE) is a tool for emulating networks on one or more machines. You can connect these emulated networks to live networks. CORE consists of a GUI for drawing topologies of lightweight virtual machines, and Python modules for scripting network emulation.

This is a network simulator. This is meant to be user friendly as well as feature rich. This field lacks software with good user interface. Even the commercial softwares in this field is quite lame. This aimed to go along Multisim(R) and Blender(R) form

The program Virtual Network Simulator for Web 2.0 is a tool that allows the simulation of assembly and configuration of computer network projects, virtual or physical. It also simulate the sending of a package between the hots of origin and destinati

Graphical Network Simulator-3 is a network software emulator first released in 2008. It allows the combination of virtual and real devices, used to simulate complex networks. It uses Dynamips emulation software to simulate Cisco IOS.

Wireless Sensor Network Simulator Castalia is a simulator for Wireless Sensor Networks (WSN), Body Area Networks (BAN) and generally networks of low-power embedded devices. It is based on the OMNeT++ platform and can be used by researchers and developers who want to test their distributed algorithms and/or protocols in realistic wireless channel and radio models, with a realistic node behaviour especially relating to access of the radio. Castalia can also be used to evaluate different platform characteristics for specific applications, since it is highly parametric, and can simulate a wide range of platforms.

Deploys a secure VPC on AWS with public/private subnets using Terraform. This tool provides a calculator and generator for AWS VPC Terraform configurations. It allows you to quickly scaffold a secure VPC with multiple subnets. Quick Start To use this tool, run the generator: bash # Generate a VPC with 3 subnets python lab_runner.py vpc-gen --cidr 10.10.0.0/16 --subnets 3 --output my_vpc.tf

NeMo is a high-performance spiking neural network simulator which simulates networks of Izhikevich neurons on CUDA-enabled GPUs. NeMo is a C++ class library, with additional interfaces for pure C, Python, and Matlab.

UM-OLSR is an OLSR (Optimized Link State Routing protocol) implementation for the ns2 network simulator.

For new versions, check https://github.com/Darkkey/javaNetSim javaNetSim (Java Network Simulator) - it's a fork of a project jFirewallSim. The main goal of javaNetSim is creating a software to simulate various TCP/IP networks based on Ethernet, WiFi, PPP, etc...

This project provides DiffServ simulations improvements for the Network Simulator 2. The current version works for NS2 version 2.29

GatcomSUMO is a multi-platform application that facilitates the building of VANET simulations using the SUMO traffic simulator and the OMNeT++ network simulator. The tasks related to the generation of abstract or synthetic scenarios, the download and conversion of network scenarios based on real maps (e.g. from OpenStreetMap), the route creation process, and the vehicles mobility modelling are integrated in a user-friendly graphical interface. This tool performs the necessary actions by invoking the set of utilities included in SUMO, saving the user from typing complex command-line orders. GatcomSUMO visualizes the network and routes easily, and they are created in such a way to meet the requirements of the involved simulators, in particular OMNeT++, in order to avoid misunderstandings and runtime errors. The application allows the conversion of the spatial coordinates used by SUMO into those used by OMNeT++, which is essential to place fixed elements like RSUs.

Liowsn (Linux for works with WSN) was created to assist teachers, students and people involved with projects of Wireless Sensor Network (WSN). The operating system is Ubuntu 9.10 remastered and it contains several tools for simulation and testing installed to work with WSN. Discussion Group: http://groups.google.com/group/liowsn-users User manual: https://dl.dropbox.com/u/4912938/LiowsnManual_EN.pdf (English Version) https://dl.dropbox.com/u/4912938/LiowsnManual_PT.pdf (Portuguese Version)

tlearn is a backpropagation neural network simulator, written by Jeff Elman. xtlearn is a version of tlearn for the X Window System. OSXtlearn is xtlearn wrapped in a MacOSX application bundle that runs ons MacOSX 10.5 or higher and that requires XQuartz. OSXtlearn is created by Harm Brouwer.

2023-03-20: the latest version of the XG-PON model for ns-3.35 is in GitHub (https://github.com/ajerikko/xgpon4ns3-v3/releases/tag/v3.1). It contains major changes to example code. Please use it for new activities. 10-Gigabit-capable Passive Optical Network (XG-PON), is regarded as one of the key technologies for future Internet access networks. This project develops an XG-PON module for the NS-3 network simulator - a G987 (ITU-T) standards-compliant, configurable, and extensible module that can simulate XG-PON that can support a wide range of research topics. This work was supported in part by SFI through CTVR. Please use the below citation when using this module: Arokkiam, Jerome A, Pedro Alvarez, Xiuchao Wu, Kenneth N Brown, Cormac J Sreenan, Marco Ruffini, Nicola Marchetti, Linda Doyle, and David Payne. “Design, Implementation, and Evaluation of an XG-PON Module for the Ns-3 Network Simulator.” SIMULATION 93, no. 5 (May 2017): 409–26. doi: 10.1177/0037549716682093

Netsim is a mobile ad hoc network simulator targeted at large heterogeneous node configurations. It is written in Java and is easily extensible through its modular concept.

This is an implementation of the Granular Neural Network architecture defined by S. Dick, A. Tappenden, C. Badke, O. Olarewaju. It is provided for the use of the public, and the convenience of researchers who may wish to develop or use this new system.

AHANNS enables the user to create and train artificial neural networks. Training is carried out by an implementation of back propagation learning algorithm. See the documentation for details. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

AWNetS(Aibear Wireless Network Simulator)is a C++ written,fire-new discrete-event wireless network simulator which integrates network and communication simulations.It also focuses on various kinds of wireless networks including satellite and HAP.

Implemetaion the protocol AntHocNet in Network Simulator 2 (NS-2)

In an optical network scenario which consists of multiple nodes (whiteboxes) at its edges and ROADMs in-between, the coherent transceiver average laser configuration time is improved. The process is evaluated according to a testbed setup. This is facilitated in the appropriate lab equipment (or via simulation when required). For that purpose, a software agent (Netconf server) residing at the whiteboxes, is developed receiving input from the Software-Defined Networking (SDN) packet controller (PacketCTL - a Netconf client). Then, configuration of the local transceiver laser frequencies of the controlled pluggable devices takes place, for facilitating the connectivity in-between the ROADM network. Also, the agent records and reports back telemetry data (feedback) which is used by the PacketCTL's resource-allocating mechanism to improve efficiency within the network topology.

BlueHoc is an open source Bluetooth technology simulator. It simulates the baseband and link layers of the Bluetooth specifications.

A neuronal network simulator using NeuroML (as generated by neuroConstruct) for network description. Conductance- and rate-based Hodgkin-Huxley neuron models, and a number of other neurons & synapses. A 5-6-order RK method is used for integration.

Cervelletto is a neural network simulator. It uses a new neural model based on biological, neurological and psychological studies. [it's not yet completed... just give me some weeks! sorry!]