Adaptive predictive control for software systems
(2015) 1st International Workshop on Control Theory for Software Engineering p.17-21- Abstract
- Self-adaptive software systems are designed to support a number of alternative solutions for fulfilling their requirements. These define an adaptation space. During operation, a self-adaptive system monitors its performance and when it finds that its requirements are not fulfilled, searches its adaptation space to select a best adaptation. Two major problems need to be addressed during the selection process: (a) Handling environmental uncertainty in determining the impact of an adaptation; (b) maintain an optimal equilibrium among conflicting requirements. This position paper investigates the application of Adaptive Model Predictive Control ideas from Control Theory to design self-adaptive software that makes decisions by predicting its... (More)
- Self-adaptive software systems are designed to support a number of alternative solutions for fulfilling their requirements. These define an adaptation space. During operation, a self-adaptive system monitors its performance and when it finds that its requirements are not fulfilled, searches its adaptation space to select a best adaptation. Two major problems need to be addressed during the selection process: (a) Handling environmental uncertainty in determining the impact of an adaptation; (b) maintain an optimal equilibrium among conflicting requirements. This position paper investigates the application of Adaptive Model Predictive Control ideas from Control Theory to design self-adaptive software that makes decisions by predicting its future performance for alternative adaptations and selects ones that minimize the cost of requirement failures using quantitative information. The technical details of our proposal are illustrated through the meeting-scheduler exemplar. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7852845
- author
- Konstantinos, Angelopoulos ; Papadopoulos, Alessandro Vittorio LU and Mylopoulos, John
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 1st International Workshop on Control Theory for Software Engineering
- pages
- 17 - 21
- publisher
- Association for Computing Machinery (ACM)
- conference name
- 1st International Workshop on Control Theory for Software Engineering
- conference location
- Bergamo, Italy
- conference dates
- 2015-08-31
- external identifiers
-
- scopus:84960467450
- ISBN
- 978-1-4503-3814-1
- DOI
- 10.1145/2804337.2804340
- project
- EIT_VR CLOUD Cloud Control
- language
- English
- LU publication?
- yes
- id
- c9826b68-f8f5-4b69-b03b-2c0bbf2dab96 (old id 7852845)
- date added to LUP
- 2016-04-04 13:23:37
- date last changed
- 2024-06-09 04:33:28
@inproceedings{c9826b68-f8f5-4b69-b03b-2c0bbf2dab96, abstract = {{Self-adaptive software systems are designed to support a number of alternative solutions for fulfilling their requirements. These define an adaptation space. During operation, a self-adaptive system monitors its performance and when it finds that its requirements are not fulfilled, searches its adaptation space to select a best adaptation. Two major problems need to be addressed during the selection process: (a) Handling environmental uncertainty in determining the impact of an adaptation; (b) maintain an optimal equilibrium among conflicting requirements. This position paper investigates the application of Adaptive Model Predictive Control ideas from Control Theory to design self-adaptive software that makes decisions by predicting its future performance for alternative adaptations and selects ones that minimize the cost of requirement failures using quantitative information. The technical details of our proposal are illustrated through the meeting-scheduler exemplar.}}, author = {{Konstantinos, Angelopoulos and Papadopoulos, Alessandro Vittorio and Mylopoulos, John}}, booktitle = {{Proceedings of the 1st International Workshop on Control Theory for Software Engineering}}, isbn = {{978-1-4503-3814-1}}, language = {{eng}}, pages = {{17--21}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{Adaptive predictive control for software systems}}, url = {{https://lup.lub.lu.se/search/files/6108886/8515010.pdf}}, doi = {{10.1145/2804337.2804340}}, year = {{2015}}, }