A DSL-Supported Workflow for the Automated Assembly of Large Stochastic Models

Abstract

Dependability and performance analysis of modern systems is facing great challenges: their scale is growing, they are becoming massively distributed, interconnected, and evolving. Such complexity makes model-based assessment a difficult and time-consuming task. For the evaluation of large systems, reusable submodels are typically adopted as an effective way to address the complexity and improve the maintanability of models. Approaches based on Stochastic Petri Nets often compose submodels by state-sharing, following predefined "patterns", depending on the scenario of interest. However, such composition patterns are typically not formalized. Clearly defining libraries of reusable submodels, together with valid patterns for their composition, would allow complex models to be automatically assembled, based on a high-level description of the scenario to be evaluated. The contribution of this paper to this problem is twofold: on one hand we describe our workflow for the automated generation of large performability models, on the other hand we introduce the TMDL language, a DSL to concretely support the workflow. After introducing the approach and the language, we detail their implementation within the Eclipse modeling platform, and briefly show its usage through an example.

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