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.
Title
Scalable Models Using Model Transformation
Published
2008-07-13
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2008-85
Type
Text
Extent
17 p
Archive
The Engineering Library
Usage Statement
Researchers may make free and open use of the UC Berkeley Library’s digitized public domain materials. However, some materials in our online collections may be protected by U.S. copyright law (Title 17, U.S.C.). Use or reproduction of materials protected by copyright beyond that allowed by fair use (Title 17, U.S.C. § 107) requires permission from the copyright owners. The use or reproduction of some materials may also be restricted by terms of University of California gift or purchase agreements, privacy and publicity rights, or trademark law. Responsibility for determining rights status and permissibility of any use or reproduction rests exclusively with the researcher. To learn more or make inquiries, please see our permissions policies (https://www.lib.berkeley.edu/about/permissions-policies).
