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Adaptive predictive control for software systems

Konstantinos, Angelopoulos; Papadopoulos, Alessandro Vittorio LU and Mylopoulos, John (2015) 1st International Workshop on Control Theory for Software Engineering In Proceedings of the 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:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Proceedings of the 1st International Workshop on Control Theory for Software Engineering
pages
17 - 21
publisher
Association for Computing Machinery, Inc
conference name
1st International Workshop on Control Theory for Software Engineering
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
2015-09-04 09:27:49
date last changed
2016-10-13 04:54:41
@misc{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},
  isbn         = { 978-1-4503-3814-1},
  language     = {eng},
  pages        = {17--21},
  publisher    = {ARRAY(0x7b8bbd0)},
  series       = {Proceedings of the 1st International Workshop on Control Theory for Software Engineering},
  title        = {Adaptive predictive control for software systems},
  url          = {http://dx.doi.org/10.1145/2804337.2804340},
  year         = {2015},
}