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

Garcia, Luis; Samin, Huma; Bencomo, Nelly

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, 19(2), 1-44. https://doi.org/10.1145/3643889

• Journal Article Type: Article
• Acceptance Date: Nov 30, 2023
• Online Publication Date: Feb 9, 2024
• Publication Date: Apr 20, 2024
• Deposit Date: Feb 7, 2024
• Publicly Available Date: Feb 12, 2024
• Journal: ACM Transactions on Autonomous and Adaptive Systems
• Print ISSN: 1556-4665
• Electronic ISSN: 1556-4703
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 2
• Article Number: 11
• Pages: 1-44
• DOI: https://doi.org/10.1145/3643889
• Public URL: https://durham-repository.worktribe.com/output/2228526