Arrow: Automatic runtime reappraisal of weights for self-adaptation

Luis H. Garcia Paucar, Nelly Bencomo, Kevin Kam Fung Yuen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


[Context/Motivation] Decision-making for self-adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) and the costs-benefits analysis of the alternative solutions. Usually, it is required the specification of the weights (a.k.a. preferences) associated with the NFRs and decision-making strategies. These preferences are traditionally defined at design-time. [Questions/Problems] A big challenge is the need to deal with unsuitable preferences, based on empirical evidence available at runtime, and which may not agree anymore with previous assumptions. Therefore, new techniques are needed to systematically reassess the current preferences according to empirical evidence collected at runtime. [Principal ideas/ results] We present ARRoW (Automatic Runtime Reappraisal of Weights) to support the dynamic update of preferences/weights associated with the NFRs and decision-making strategies in SAS, while taking into account the current levels of satisficement that NFRs can reach during the system's operation. [Contribution] To developed ARRoW, we have extended the Primitive Cognitive Network Process (P-CNP), a version of the Analytical Hierarchy Process (AHP), to enable the handling and update of weights during runtime. Specifically, in this paper, we show a formalization for the specification of the decision-making of a SAS in terms of NFRs, the design decisions and their corresponding weights as a P-CNP problem. We also report on how the P-CNP has been extended to be used at runtime. We show how the propagation of elements of P-CNP matrices is performed in such a way that the weights are updated to therefore, improve the levels of satisficement of the NFRs to better match the current environment during runtime. ARRoW leverages the Bayesian learning process underneath, which on the other hand, provides the mechanism to get access to evidence about the levels of satisficement of the NFRs. The experiments have been applied to a case study of the networking application domain where the decision-making has been improved.

Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)9781450359337
Publication statusPublished - 2019
Externally publishedYes
Event34th Annual ACM Symposium on Applied Computing, SAC 2019 - Limassol, Cyprus
Duration: 8 Apr 201912 Apr 2019

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F147772


Conference34th Annual ACM Symposium on Applied Computing, SAC 2019


  • AHP
  • Bayesian evidence
  • Decision-making
  • Non-functional properties
  • Runtime models
  • Self-adaptation
  • Uncertainty

ASJC Scopus subject areas

  • Software


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