ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation

Nelly Bencomo, Luis Garcia Paucar, Kevin Kam Fung Yuen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

[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 publicationThe 12th Edition of the Requirements Engineering Track (RE-Track'19) is part of the 34rd ACM Symposium on Applied Computing. SAC 2019
PublisherACM
Pages1584-1591
Number of pages8
ISBN (Print)978-1-4503-5933-7/19/04
DOIs
Publication statusPublished - 8 Apr 2019
Event34th ACM/SIGAPP Symposium On Applied Computing - Limassol, Cyprus
Duration: 8 Apr 201912 Apr 2019

Conference

Conference34th ACM/SIGAPP Symposium On Applied Computing
CountryCyprus
CityLimassol
Period8/04/1912/04/19

Fingerprint

Decision making
Adaptive systems
Specifications
Cost benefit analysis
Experiments

Bibliographical note

© 2019 Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than ACM
must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,
to post on servers or to redistribute to lists, requires prior specific permission and/or a
fee. Request permissions from permissions@acm.org.

Keywords

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

Cite this

Bencomo, N., Garcia Paucar, L., & Yuen, K. K. F. (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 (pp. 1584-1591). ACM. https://doi.org/10.1145/3297280.3299743
Bencomo, Nelly ; Garcia Paucar, Luis ; Yuen, Kevin Kam Fung . / ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation. The 12th Edition of the Requirements Engineering Track (RE-Track'19) is part of the 34rd ACM Symposium on Applied Computing. SAC 2019. ACM, 2019. pp. 1584-1591
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Bencomo, N, Garcia Paucar, L & Yuen, KKF 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, pp. 1584-1591, 34th ACM/SIGAPP Symposium On Applied Computing, Limassol, Cyprus, 8/04/19. https://doi.org/10.1145/3297280.3299743

ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation. / Bencomo, Nelly; Garcia Paucar, Luis; Yuen, Kevin Kam Fung .

The 12th Edition of the Requirements Engineering Track (RE-Track'19) is part of the 34rd ACM Symposium on Applied Computing. SAC 2019. ACM, 2019. p. 1584-1591.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Yuen, Kevin Kam Fung

N1 - © 2019 Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

PY - 2019/4/8

Y1 - 2019/4/8

N2 - [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.

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Bencomo N, Garcia Paucar L, Yuen KKF. 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. 2019. p. 1584-1591 https://doi.org/10.1145/3297280.3299743