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Towards scalable search-based model engineering with MDEOptimiser scale

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publicationProceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019
EditorsLoli Burgueno, Loli Burgueno, Alexander Pretschner, Sebastian Voss, Michel Chaudron, Jorg Kienzle, Markus Volter, Sebastien Gerard, Mansooreh Zahedi, Erwan Bousse, Arend Rensink, Fiona Polack, Gregor Engels, Gerti Kappel
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages189-195
Number of pages7
ISBN (Electronic)9781728151250
DOIs
Publication statusE-pub ahead of print - 15 Sep 2019
Event22nd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019 - Munich, Germany
Duration: 15 Sep 201920 Sep 2019

Conference

Conference22nd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019
CountryGermany
CityMunich
Period15/09/201920/09/2019

Documents

King's Authors

Abstract

Running scientific experiments using search-based model engineering (SBME) tools is a complex task, that poses a number of challenges, starting from defining an experiment workflow, to parameter tuning, finding optimal computational resources to run on, collecting and interpreting metrics and making the entire process easily reproducible. Despite the proliferation of easily accessible hardware, as a result of the increased availability of infrastructure-as-a-service providers, many SBME tools are rarely using this technology for accelerating experimentation. Running many experiments on a single machine implies much longer waiting times and reduces the ability to increase the speed of iterations when doing SBME research, thus, slowing down the entire process. In this paper, we introduce a domain-specific language (DSL) and a framework that can be used to configure and run experiments at scale, on cloud infrastructure, in a reproducible way. We will describe our DSL and framework architecture along with an example to showcase how a case study can be evaluated using two different model optimisation tools.

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