Scalable Models Using Model Transformation

Higher-order model composition can be employed as a mechanism for scalable model construction. By creating a description that manipulates model fragments as first-class objects, designers’ work of model creation and maintenance can be greatly simplified. In this paper, we present our approach to higher-order model composition based on model transformation. We define basic transformation rules to operate on the graph structures of actor models. The composition of basic transformation rules with heterogeneous models of computation form complex transformation systems, which we use to construct large models. We argue that our approach is more visual than the traditional approaches using textual model descriptions. It also has the advantage of allowing to dynamically modify models and to execute them on the fly. Our arguments are supported by a concrete example of constructing a distributed model of arbitrary size.