Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes

Luis Hernan Garcia Paucar; Nelly Bencomo

doi:10.1109/SASO.2019.00011

Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes

Luis Hernan Garcia Paucar, Nelly Bencomo

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

9 Citations (Scopus)

77 Downloads (Pure)

Abstract

Runtime models support decision-making and reasoning for self-adaptation based on both design-time knowledge and information that may emerge at runtime. In this paper, we demonstrate a novel use of Partially Observable Markov Decision Processes (POMDPs) as runtime models to support the decision-making of a Self Adaptive System (SAS) in the context of the MAPE-K loop. The trade-off between the non-functional requirements (NFRs) has been embodied as a POMDP in the context of the MAPE-K loop. Using Bayesian learning, the levels of satisficement of the NFRs are inferred and updated during execution in the form of runtime models in the Knowledge Base. We evaluate our work with a case study of the networking application domain.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
Publisher	IEEE
Pages	11-16
Number of pages	6
Volume	2019-June
ISBN (Electronic)	978-1-7281-2731-6
ISBN (Print)	978-1-7281-2732-3
DOIs	https://doi.org/10.1109/SASO.2019.00011
Publication status	Published - 1 Jun 2019
Event	2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) - Umea, Sweden Duration: 16 Jun 2019 → 20 Jun 2019

Publication series

Name	2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
Publisher	IEEE
ISSN (Print)	1949-3673
ISSN (Electronic)	1949-3681

Conference

Conference	2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
Period	16/06/19 → 20/06/19

Bibliographical note

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

Markov Processes
Runtime models
decision making
non-functional requirements
self-adaptation
uncertainty

Access to Document

10.1109/SASO.2019.00011

Knowledge Base K ModelsAccepted author manuscript, 887 KB

Cite this

Garcia Paucar, L. H., & Bencomo, N. (2019). Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes. In Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019 (Vol. 2019-June, pp. 11-16). Article 8780528 (2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)). IEEE. https://doi.org/10.1109/SASO.2019.00011

Garcia Paucar, Luis Hernan ; Bencomo, Nelly. / Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes. Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019. Vol. 2019-June IEEE, 2019. pp. 11-16 (2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)).

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Garcia Paucar, LH & Bencomo, N 2019, Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes. in Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019. vol. 2019-June, 8780528, 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), IEEE, pp. 11-16, 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 16/06/19. https://doi.org/10.1109/SASO.2019.00011

Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes. / Garcia Paucar, Luis Hernan; Bencomo, Nelly.
Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019. Vol. 2019-June IEEE, 2019. p. 11-16 8780528 (2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)).

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

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AU - Garcia Paucar, Luis Hernan

AU - Bencomo, Nelly

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2019/6/1

Y1 - 2019/6/1

N2 - Runtime models support decision-making and reasoning for self-adaptation based on both design-time knowledge and information that may emerge at runtime. In this paper, we demonstrate a novel use of Partially Observable Markov Decision Processes (POMDPs) as runtime models to support the decision-making of a Self Adaptive System (SAS) in the context of the MAPE-K loop. The trade-off between the non-functional requirements (NFRs) has been embodied as a POMDP in the context of the MAPE-K loop. Using Bayesian learning, the levels of satisficement of the NFRs are inferred and updated during execution in the form of runtime models in the Knowledge Base. We evaluate our work with a case study of the networking application domain.

AB - Runtime models support decision-making and reasoning for self-adaptation based on both design-time knowledge and information that may emerge at runtime. In this paper, we demonstrate a novel use of Partially Observable Markov Decision Processes (POMDPs) as runtime models to support the decision-making of a Self Adaptive System (SAS) in the context of the MAPE-K loop. The trade-off between the non-functional requirements (NFRs) has been embodied as a POMDP in the context of the MAPE-K loop. Using Bayesian learning, the levels of satisficement of the NFRs are inferred and updated during execution in the form of runtime models in the Knowledge Base. We evaluate our work with a case study of the networking application domain.

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KW - decision making

KW - non-functional requirements

KW - self-adaptation

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Y2 - 16 June 2019 through 20 June 2019

ER -

Garcia Paucar LH, Bencomo N. Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes. In Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019. Vol. 2019-June. IEEE. 2019. p. 11-16. 8780528. (2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)). doi: 10.1109/SASO.2019.00011

Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes

Abstract

Publication series

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Bibliographical note

Keywords

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