Incremental runtime-generation of optimisation problems using RAG-controlled rewriting
(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.
(Less)
- author
- Schöne, René ; Götz, Sebastian ; Aßmann, Uwe and Bürger, Christoff LU
- organization
- publishing date
- 2016
- type
- Contribution to journal
- publication status
- published
- subject
- in
- CEUR Workshop Proceedings
- volume
- 1742
- pages
- 9 pages
- publisher
- CEUR-WS
- 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
- 2022-03-01 19:57:23
@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}},
publisher = {{CEUR-WS}},
series = {{CEUR Workshop Proceedings}},
title = {{Incremental runtime-generation of optimisation problems using RAG-controlled rewriting}},
url = {{http://ceur-ws.org/Vol-1742/MRT16_paper_5.pdf}},
volume = {{1742}},
year = {{2016}},
}