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)
Links:
- 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.
