ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation

Nelly Bencomo; Luis Hernan Garcia Paucar; Kevin Kam Fung  Yuen

doi:10.1145/3297280.3299743

ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation

Nelly Bencomo, Luis Hernan Garcia Paucar, Kevin Kam Fung Yuen

Research output: Chapter in Book/Published conference output › Conference publication

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 language	English
Title of host publication	The 12th Edition of the Requirements Engineering Track (RE-Track'19) is part of the 34rd ACM Symposium on Applied Computing. SAC 2019
Publisher	ACM
Pages	1584-1591
Number of pages	8
ISBN (Print)	978-1-4503-5933-7/19/04
DOIs	https://doi.org/10.1145/3297280.3299743
Publication status	Published - 8 Apr 2019
Event	34th ACM/SIGAPP Symposium On Applied Computing - Limassol, Cyprus Duration: 8 Apr 2019 → 12 Apr 2019

Conference

Conference	34th ACM/SIGAPP Symposium On Applied Computing
Country/Territory	Cyprus
City	Limassol
Period	8/04/19 → 12/04/19

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

Access to Document

10.1145/3297280.3299743

Cite this

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title = "ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation",

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.",

keywords = "AHP, Bayesian evidence, Decision-making, Non-functional properties, Runtime models, Self-adaptation, Uncertainty",

author = "Nelly Bencomo and {Garcia Paucar}, {Luis Hernan} and Yuen, {Kevin Kam Fung}",

note = "{\textcopyright} 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.; 34th ACM/SIGAPP Symposium On Applied Computing ; Conference date: 08-04-2019 Through 12-04-2019",

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Bencomo, N, Garcia Paucar, LH & 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 Hernan; 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/Published conference output › Conference publication

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AU - Bencomo, Nelly

AU - Garcia Paucar, Luis Hernan

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.

AB - [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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KW - Bayesian evidence

KW - Decision-making

KW - Non-functional properties

KW - Runtime models

KW - Self-adaptation

KW - Uncertainty

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SP - 1584

EP - 1591

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

PB - ACM

T2 - 34th ACM/SIGAPP Symposium On Applied Computing

Y2 - 8 April 2019 through 12 April 2019

ER -

ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation

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Keywords

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