Using Reference Attribute Grammar-Controlled Rewriting for Energy Auto-Tuning
(2015) 10th International Workshop on Models@run.time 1474. p.31-40- Abstract
- Cyber-physical systems react on events reported by sensors and interact with objects of the real world according to their current state and their view of the world. This view is naturally represented by a model which is continuously analysed and updated at runtime. Model analyses should be ideally concise and efficient, requiring well-founded, comprehensible implementations with efficient reasoning mechanisms. In this paper, we apply reference attribute grammar controlled rewriting to concisely implement the runtime model of an auto-tuning case study for energy optimization. Attribute functions are used to interactively perform analyses. In case of an update, our system incrementally—and, thus, efficiently—recomputes depending analyses.... (More)
- Cyber-physical systems react on events reported by sensors and interact with objects of the real world according to their current state and their view of the world. This view is naturally represented by a model which is continuously analysed and updated at runtime. Model analyses should be ideally concise and efficient, requiring well-founded, comprehensible implementations with efficient reasoning mechanisms. In this paper, we apply reference attribute grammar controlled rewriting to concisely implement the runtime model of an auto-tuning case study for energy optimization. Attribute functions are used to interactively perform analyses. In case of an update, our system incrementally—and, thus, efficiently—recomputes depending analyses. Since reference attribute grammar controlled rewriting builds the required dependency graphs automatically, incremental analysis comes for free. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7890470
- author
- Bürger, Christoff LU ; Mey, Johannes ; Schöne, René and Karol, Sven
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- attribute grammar, graph rewriting, incremental analyses, runtime model, cyber-physical system, energy auto-tuning
- host publication
- CEUR Workshop Proceedings (CEUR-WS.org)
- editor
- Götz, Sebastian ; Bencomo, Nelly ; Blair, Gordon and Song, Hui
- volume
- 1474
- pages
- 10 pages
- publisher
- CEUR-WS
- conference name
- 10th International Workshop on Models@run.time
- conference location
- Ottawa, Canada
- conference dates
- 2015-09-29
- external identifiers
-
- scopus:84954465991
- ISSN
- 1613-0073
- language
- English
- LU publication?
- yes
- additional info
- urn:nbn:de:0074-1474-1
- id
- 8f593af9-7e8b-4ec4-9a03-529b4b639013 (old id 7890470)
- alternative location
- http://ceur-ws.org/Vol-1474/
- http://ceur-ws.org/Vol-1474/MRT15_paper_1.pdf
- date added to LUP
- 2016-04-01 14:13:54
- date last changed
- 2022-01-27 23:34:16
@inproceedings{8f593af9-7e8b-4ec4-9a03-529b4b639013, abstract = {{Cyber-physical systems react on events reported by sensors and interact with objects of the real world according to their current state and their view of the world. This view is naturally represented by a model which is continuously analysed and updated at runtime. Model analyses should be ideally concise and efficient, requiring well-founded, comprehensible implementations with efficient reasoning mechanisms. In this paper, we apply reference attribute grammar controlled rewriting to concisely implement the runtime model of an auto-tuning case study for energy optimization. Attribute functions are used to interactively perform analyses. In case of an update, our system incrementally—and, thus, efficiently—recomputes depending analyses. Since reference attribute grammar controlled rewriting builds the required dependency graphs automatically, incremental analysis comes for free.}}, author = {{Bürger, Christoff and Mey, Johannes and Schöne, René and Karol, Sven}}, booktitle = {{CEUR Workshop Proceedings (CEUR-WS.org)}}, editor = {{Götz, Sebastian and Bencomo, Nelly and Blair, Gordon and Song, Hui}}, issn = {{1613-0073}}, keywords = {{attribute grammar; graph rewriting; incremental analyses; runtime model; cyber-physical system; energy auto-tuning}}, language = {{eng}}, pages = {{31--40}}, publisher = {{CEUR-WS}}, title = {{Using Reference Attribute Grammar-Controlled Rewriting for Energy Auto-Tuning}}, url = {{https://lup.lub.lu.se/search/files/3857069/8057664.pdf}}, volume = {{1474}}, year = {{2015}}, }