Title
Scalable Armies of Model Clones through Data Sharing.
Abstract
Cloning a model is usually done by duplicating all its runtime objects into a new model. This approach leads to memory consumption problems for operations that create and manipulate large quantities of clones (e.g., design space exploration). We propose an original approach that exploits the fact that operations rarely modify a whole model. Given a set of immutable properties, our cloning approach determines the objects and fields that can be shared between the runtime representations of a model and its clones. Our generic cloning algorithm is parameterized with three strategies that establish a trade-off between memory savings and the ease of clone manipulation. We implemented the strategies within the Eclipse Modeling Framework (EMF) and evaluated memory footprints and computation overheads with 100 randomly generated metamodels and models. Results show a positive correlation between the proportion of shareable properties and memory savings, while the worst median overhead is 9,5% when manipulating the clones.
Year
DOI
Venue
2014
10.1007/978-3-319-11653-2_18
Lecture Notes in Computer Science
Field
DocType
Volume
Parameterized complexity,Computer science,Data sharing,Exploit,Theoretical computer science,Eclipse,Design space exploration,Scalability,Overhead (business),Computation
Conference
8767
ISSN
Citations 
PageRank 
0302-9743
5
0.49
References 
Authors
7
3
Name
Order
Citations
PageRank
Erwan Bousse1467.96
Benoît Combemale242346.61
Benoit Baudry32000118.08