Distributing Scenario-Based Models: A Replicate-and-Project Approach

Shlomi Steinberg

Joel Greenyer

Daniel Gritzner

David Harel

Guy Katz

Assaf Marron

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BibTex ×

@inproceedings{Steinberg_2017,
 	doi = {10.5220/0006271301820195},
 	url = {https://doi.org/10.5220%2F0006271301820195},
 	year = 2017,
 	publisher = {{SCITEPRESS} - Science and Technology Publications},
 	author = {Shlomi Steinberg and Joel Greenyer and Daniel Gritzner and David Harel and Guy Katz and Assaf Marron},
 	title = {Distributing Scenario-based Models: A Replicate-and-Project Approach},
 	booktitle = {Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development}
 }

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MODELSWARD 2017 — Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development

Publication date: February 19th, 2017
doi: 10.5220/0006271301820195

Abstract

In recent years, scenario-based modeling has been proposed to help mitigate some of the underlying difficulties in modeling complex reactive systems, by allowing modelers to specify system behavior in a way that is intuitive and directly executable. This modeling approach simplifies the specification of systems that include events occurring in distinct system components. However, when these system components are physically distributed, executing the scenario-based model requires inter-component coordination that may negatively affect system performance or robustness. We describe a technique that aims to reduce the amount of joint event-selection decisions that require coordination and synchronization among distributed system components. The technique calls for replicating the entire scenario-based executable specification in each of the components,and then transforming it in a component-specific manner that induces the required differences in execution while reducing synchronization requirements. In addition to advantages in streamlining design and improving performance, our approach captures the fact that in certain “smart” distributed systems it is often required that components know what rules govern the behavior of other components. Our evaluation of the technique shows promising results.