Update: This tool is no longer maintained and supported.
DNI - Descartes Network Infrastructures Modeling
DNI is a family of meta-models designed for modeling the performance of communication networks. DNI is tightly related to DML and the main modeling domain are data center networks. A user can model network topology, switches, routers, servers, virtual machines, deployment of software, network protocols, routes, flow-based configuration and other relevant network parts. DNI can be used to model any type of network as it was designed to be as generic as possible.
The DNI Meta-model has been designed to support describing the most relevant performance influencing factors that occur in practice while abstracting fine-granular low level protocol details. Instances of the DNI Meta-model (DNI models) are automatically transformed to predictive stochastic models (e.g., product-form queueing networks or stochastic simulation models) by means of model-to-model transformations.
The DNI consists of the following elements:
- DNI and miniDNI meta-models – written in Emfatic and Ecore
- Scripts for generating DNI model instances – written in EOL
- Set of model-to-model transformations between the meta-models and to selected predictive simulation models (solvers) – written in ETL
- Default tree editor for specifying the DNI and miniDNI models manually
- HUTN (Human Usable Textual Notation) text editor for DNI
- Traffic model extraction library TrafficMSD@Github (full tooling for DNI extraction: coming soon)
- Download meta-models, transformations, and examples: DNI Git repository
- Manual that explains the meta-models and transformations
If you have any questions, please contact Piotr Rygielski.
Model-Based Network Analysis and Optimization (2018, Februar).
Flexible Modeling of Data Center Networks for Capacity Management Thesis; University of Würzburg, Germany. (2017, März).
Performance Analysis of SDN Switches with Hardware and Software Flow Tables in Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools (ValueTools 2016) (2016).
Modeling and Prediction of Software-Defined Networks Performance using Queueing Petri Nets in Proceedings of the Ninth International Conference on Simulation Tools and Techniques (SIMUTools 2016) (2016). 66–75.
Automated Extraction of Network Traffic Models Suitable for Performance Simulation in Proceedings of the 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016) (2016). 27–35.
Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models in Proceedings of the Eighth International Conference on Simulation Tools and Techniques (SIMUTools 2015) (2015). 119–128.
Descartes Network Infrastructures (DNI) Manual: Meta-models, Transformations, Examples (2014).
Data Center Network Throughput Analysis using Queueing Petri Nets in 34th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2014 Workshops). 4th International Workshop on Data Center Performance, (DCPerf 2014) (2014). 100–105.
Model-Based Throughput Prediction in Data Center Networks in Proceedings of the 2nd IEEE International Workshop on Measurements and Networking (M\&N 2013) (2013). 167–172.
A Meta-Model for Performance Modeling of Dynamic Virtualized Network Infrastructures in Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE 2013) (2013). 327–330.