A Template-Based Methodology for the Specification and Automated Composition of Performability Models

Research Area: Uncategorized Year: 2020
Type of Publication: Article Keywords: decision making;formal verification;software reusability;specification languages;Web services;high-level description;generic submodels libraries;automated composition algorithm;model-driven approach;composition patterns;state sharing;state-based models;re
Authors: Leonardo Montecchi; Paolo Lollini; Andrea Bondavalli
Journal: IEEE Transactions on Reliability Volume: 69
Number: 1 Pages: 293-309
Month: March
ISSN: 1558-1721
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 to improve the maintainability of models. When using state-based models, a common approach is to define libraries of generic submodels, and then compose concrete instances by state sharing, following predefined “patterns” that depend on the class of systems being modeled. However, such composition patterns are rarely formalized, or not even documented at all. In this paper, we address this problem using a model-driven approach, which combines a language to specify reusable submodels and composition patterns, and an automated composition algorithm. Clearly defining libraries of reusable submodels, together with patterns for their composition, allows complex models to be automatically assembled, based on a high-level description of the scenario to be evaluated. This paper provides a solution to this problem focusing on: formally defining the concept of model templates, defining a specification language for model templates, defining an automated instantiation and composition algorithm, and applying the approach to a case study of a large-scale distributed system.

Resilient Computing Lab, 2011

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