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Decision Making for Self-adaptation based on Partially Observable Satisfaction of Non-Functional Requirements

Garcia, Luis; Samin, Huma; Bencomo, Nelly

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Authors

Luis Garcia

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Dr Huma Samin huma.samin@durham.ac.uk
Post Doctoral Research Associate



Abstract

Approaches that support the decision-making of self-adaptive and autonomous systems (SAS) often consider an idealized situation where (i) the system’s state is treated as fully observable by the monitoring infrastructure, and (ii) adaptation actions are assumed to have known, deterministic effects over the system. However, in practice, the system’s state may not be fully observable, and the adaptation actions may produce unexpected effects due to uncertain factors. This paper presents a novel probabilistic approach to quantify the uncertainty associated with the effects of adaptation actions on the state of a SAS. Supported by Bayesian inference and POMDPs (Partially-Observable Markov Decision Processes), these effects are translated into the satisfaction levels of the non-functional requirements (NFRs) to, therefore, drive the decision-making. The approach has been applied to two substantial case studies from the networking and Internet of Things (IoT) domains, using two different POMDP solvers. The results show that the approach delivers statistically significant improvements in supporting decision-making for SAS.

Citation

Garcia, L., Samin, H., & Bencomo, N. (2024). Decision Making for Self-adaptation based on Partially Observable Satisfaction of Non-Functional Requirements. ACM Transactions on Autonomous and Adaptive Systems, https://doi.org/10.1145/3643889

Journal Article Type Article
Acceptance Date Nov 30, 2023
Online Publication Date Feb 9, 2024
Publication Date Feb 9, 2024
Deposit Date Feb 7, 2024
Publicly Available Date Feb 12, 2024
Journal ACM Transactions on Autonomous and Adaptive Systems
Print ISSN 1556-4665
Publisher Association for Computing Machinery (ACM)
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1145/3643889
Public URL https://durham-repository.worktribe.com/output/2228526

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