Replicators: Transformations to Address Model Scalability

In Model Integrated Computing, it is desirable to evaluate different design alternatives as they relate to issues of scalability. A typical approach to address scalability is to create a base model that captures the key interactions of various components (i.e., the essential properties and connections among modeling entities). A collection of base models can be adorned with necessary information to characterize their replication. In current practice, replication is accomplished by scaling the base model manually. This is a time-consuming process that represents a source of error, especially when there are deep interactions between model components. As an alternative to the manual process, this paper presents the idea of a replicator, which is a model transformation that expands the number of elements from the base model and makes the correct connections among the generated modeling elements. The paper motivates the need for replicators through case studies taken from models supporting different domains.