Large-Scale Simulations Manager Tool for OMNeT++: Expediting Simulations and Post-Processing Analysis


In AI Posted

Usually, simulations are the first approach to evaluate wireless and mobile networks due to the difficulties involved in deploying real test scenarios. Working with simulations, testing, and validating the target network model often requires a large number of simulation runs. Consequently, there are a significant amount of outcomes to be analyzed to finally plot results. One of the most extensively used simulators for wireless and mobile networks is OMNeT++. This simulation environment provides useful tools to automate the execution of simulation campaigns, yet single-scenario simulations are also supported where the assignation of resources (i.e., CPUs) has to be declared manually. However, conducting a large number of simulations is still cumbersome and can be improved to make it easier, faster, and more comfortable to analyze. In this work, we propose a large-scale simulations framework called simulations manager for OMNeT++ (SMO). SMO allows OMNeT++ users to quickly and easily execute large-scale network simulations, hiding the tedious process of conducting big simulation campaigns. Our framework automates simulations executions, resources assignment, and post-simulation data analysis through the use of Python’s wide established statistical analysis tools. Besides, our tool is flexible and easy to adapt to many different network scenarios. Our framework is accompanied by a command-line environment allowing a fast and easy manipulation that allows users to significantly reduce the total processing time to carry out large sets of simulations about 25% of the original time. Our code and its documentation are publicly available at GitHub and on our website.

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