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Incremental runtime-generation of optimisation problems using RAG-controlled rewriting

Schöne, René; Götz, Sebastian; Aßmann, Uwe and Bürger, Christoff LU (2016) In CEUR Workshop Proceedings 1742. p.26-34
Abstract

In the era of Internet of Things, software systems need to interact with many physical entities and cope with new requirements at runtime. Self-Adaptive systems aim to tackle those challenges, often representing their context with a runtime model enabling better reasoning capabilities. However, those models quickly grow in size and need to be updated frequently with small changes due to a high number of physical entities changing constantly. This situation threatens the efficacy of analyses on such models, as they lack an efficient management of those changes leading to unnecessary computation overhead. We propose applying scalable, incremental change management of runtime models in the presence of a complex model to text... (More)

In the era of Internet of Things, software systems need to interact with many physical entities and cope with new requirements at runtime. Self-Adaptive systems aim to tackle those challenges, often representing their context with a runtime model enabling better reasoning capabilities. However, those models quickly grow in size and need to be updated frequently with small changes due to a high number of physical entities changing constantly. This situation threatens the efficacy of analyses on such models, as they lack an efficient management of those changes leading to unnecessary computation overhead. We propose applying scalable, incremental change management of runtime models in the presence of a complex model to text transformation. In this paper, we present and evaluate an example of code generation of integer linear programs. In our case study using synthesized models, we saved 35 - 83% processing time compared to a non-incremental approach. Using our approach, future self-Adaptive systems can handle and analyze large-scale runtime models, even if they change frequently.

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author
organization
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type
Contribution to journal
publication status
published
subject
in
CEUR Workshop Proceedings
volume
1742
pages
9 pages
external identifiers
  • scopus:85006167831
ISSN
1613-0073
language
English
LU publication?
yes
id
4636d146-07a6-47e6-ae22-8a638eb8e28e
alternative location
http://ceur-ws.org/Vol-1742/MRT16_paper_5.pdf
date added to LUP
2017-02-22 09:52:58
date last changed
2017-02-22 09:52:58
@article{4636d146-07a6-47e6-ae22-8a638eb8e28e,
  abstract     = {<p>In the era of Internet of Things, software systems need to interact with many physical entities and cope with new requirements at runtime. Self-Adaptive systems aim to tackle those challenges, often representing their context with a runtime model enabling better reasoning capabilities. However, those models quickly grow in size and need to be updated frequently with small changes due to a high number of physical entities changing constantly. This situation threatens the efficacy of analyses on such models, as they lack an efficient management of those changes leading to unnecessary computation overhead. We propose applying scalable, incremental change management of runtime models in the presence of a complex model to text transformation. In this paper, we present and evaluate an example of code generation of integer linear programs. In our case study using synthesized models, we saved 35 - 83% processing time compared to a non-incremental approach. Using our approach, future self-Adaptive systems can handle and analyze large-scale runtime models, even if they change frequently.</p>},
  author       = {Schöne, René and Götz, Sebastian and Aßmann, Uwe and Bürger, Christoff},
  issn         = {1613-0073},
  language     = {eng},
  pages        = {26--34},
  series       = {CEUR Workshop Proceedings},
  title        = {Incremental runtime-generation of optimisation problems using RAG-controlled rewriting},
  volume       = {1742},
  year         = {2016},
}