ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation

Bencomo, Nelly, Garcia Paucar, Luis and Yuen, Kevin Kam Fung (2019). ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation. IN: The 12th Edition of the Requirements Engineering Track (RE-Track'19) is part of the 34rd ACM Symposium on Applied Computing. SAC 2019. ACM.

Abstract

(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 designtime. [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 ofWeights) 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 NFR 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 NFR. The experiments have been applied to a case study of the networking application domain where the decision-making has been improved.

Publication DOI: https://doi.org/10.1145/3297280.3299743
Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Engineering & Applied Sciences > Computer science research group
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Event Title: 34th ACM/SIGAPP Symposium On Applied Computing
Event Type: Other
Event Dates: 2019-04-08 - 2019-04-12
Accepted Date: 2019-04-08
Published Date: 2019-04-08
Authors: Bencomo, Nelly ( 0000-0001-6895-1636)
Garcia Paucar, Luis
Yuen, Kevin Kam Fung

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