The Descartes Modeling Language (DML), formerly also referred to as Descartes Meta-Model (DMM), is an architecture-level modeling language for quality-of-service and resource management of modern dynamic IT systems and infrastructures. DML is designed to serve as a basis for self-aware systems management during operation, ensuring that system quality-of-service requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible. The term quality-of-service (QoS) is used to refer to performance (response time, throughput, scalability and efficiency) and dependability (availability, reliability and security).
The current version of DML is focused on performance and availability, however, the modeling language itself is designed in a generic fashion and it is intended to eventually support further QoS properties. An overview of DML can be found in the following paper:
A more detailed introduction to DML can be found in the following journal paper:
DML has a modular structure and is provided as a set of meta-models for describing the resource landscape, the application architecture, the adaptation points, and adaptation processes of an IT system. The meta-models can be used both in online and offline settings for performance prediction and proactive system reconfiguration during operation.
The core aspects and key features of DML are:
- Support for modeling the system architecture and behavior at different levels of abstraction
- Probabilistic modeling of dependencies between system parameters
- Detailed modeling of the resource landscape including layers like virtualization and middleware
- Support for modeling dynamic system aspects including the system reconfiguration options at run-time
- Support for modeling adaptation processes at the system architecture-level
- End-to-end approach for online performance prediction and self-aware resource management
The Descartes Modeling Language (DML) is documented in the following technical report.
The DML specification can also be found in the following two recently published PhD theses.