Projects per year
Abstract
Autonomous systems are often used in applications where environmental and internal changes may lead to requirement violations. Adapting to these changes proactively, i.e., before the violations occur, is preferable to recovering from the failures that may be caused by such violations. However, proactive adaptation needs methods for predicting requirement violations timely, accurately and with acceptable overheads. To address this need, we present a method that allows autonomous systems to predict violations of performance, dependability and other nonfunctional requirements, and therefore take preventative measures to avoid or otherwise mitigate them. Our method for predicting these autonomous system disruptions (PRESTO) comprises a design time stage and a run-time stage. At design-time, we use parametric model checking to obtain algebraic expressions that formalise the relationships between the nonfunctional properties of the requirements of interest (e.g., reliability, response time and energy use) and the parameters of the system and its environment. At run-time, we predict future changes in these parameters by applying piece-wise linear regression to online data obtained through monitoring, and we use the algebraic expressions to predict the impact of these changes on the system requirements. We demonstrate the application of PRESTO through simulation in case studies from two different domains.
Original language | English |
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Article number | 6 |
Number of pages | 25 |
Journal | ACM Transactions on Autonomous and Adaptive Systems |
Volume | 19 |
Issue number | 1 |
DOIs | |
Publication status | Published - 14 Feb 2024 |
Bibliographical note
© 2024 Copyright held by the owner/author(s).Projects
- 1 Finished
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UKRI Trustworthy Autonomous Systems Node in Resilience
Calinescu, R. (Principal investigator), Arvind, T. (Co-investigator), Cavalcanti, A. L. C. (Co-investigator), Habli, I. (Co-investigator), Thomas, A. P. (Co-investigator) & Wilson, J. C. (Co-investigator)
1/11/20 → 31/10/24
Project: Research project (funded) › Research
Datasets
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Online material for Predicting Nonfunctional Requirement Violations in Autonomous Systems
Fang, X. (Creator), Getir Yaman, S. (Creator), Calinescu, R. (Creator), Wilson, J. C. (Creator) & Paterson, C. (Creator), GitHub, 2023
https://github.com/xinwei2124/TAAS
Dataset