poster

Self-accounting in architecture-based self-adaptation

Authors:

Raffaela Mirandola,

Elvinia Riccobene,

Patrizia ScandurraAuthors Info & Claims

ECSA '19: Proceedings of the 13th European Conference on Software Architecture - Volume 2

Pages 14 - 17

https://doi.org/10.1145/3344948.3344957

Published: 09 September 2019 Publication History

Abstract

This paper proposes a work-in-progress approach regarding qualities of the managing layer in architecture-based self-adaptation. In particular, we establish the notion of self-accounting as self-* property and we present an inductive method, based on the structure of the MAPE pattern of the adaptation layer, to evaluate the cost of the adaptation logic in terms of latency time and availability of the managing system. We also show how the MSL (MAPE Specification Language), a language for modeling the adaptation layer in terms of MAPE patterns, has been extended to annotate MAPE components with values for these quality properties, so allowing the computation of the cost function and endowing an adaptation layer with a value for its self-accounting property.

References

[1]

P. Arcaini, R. Mirandola, E. Riccobene, and P. Scandurra. A DSL for MAPE patterns representation in self-adapting systems. In 12th European Conference on Software Architecture, ECSA 2018 Proceedings, LNCS 11048, pages 3--19, Springer.

[2]

P. Arcaini, R. Mirandola, E. Riccobene, and P. Scandurra. A pattern-oriented design framework for self-adaptive software systems. In 2019 IEEE Int. Conf. on Software Architecture, ICSA Workshops 2019, Hamburg, Germany, 2019.

[3]

S. Bernardi, J. Merseguer, and D. C. Petriu. A dependability profile within MARTE. Software and System Modeling, 10(3):313--336, 2011.

Digital Library

[4]

A. Berns and S. Ghosh. Dissecting self-* properties. In 2009 Third IEEE Int. Conf. on Self-Adaptive and Self-Organizing Systems, pages 10--19, Sep. 2009.

Digital Library

[5]

Y. Brun et al. Engineering self-adaptive systems through feedback loops. In Software Engineering for Self-Adaptive Systems, LNCS 5525, pages 48--70, 2009, Springer, Berlin, Heidelberg.

Digital Library

[6]

J. Cámara, A. Lopes, D. Garlan, and B. R. Schmerl. Adaptation impact and environment models for architecture-based self-adaptive systems. Sci. Comput. Program., 127:50--75, 2016.

Digital Library

[7]

M. Caporuscio, V. Grassi, M. Marzolla, and R. Mirandola. Goprime: A fully decentralized middleware for utility-aware service assembly. IEEE Trans. Software Eng., 42(2):136--152, 2016.

Digital Library

[8]

T. Chen, R. Bahsoon, S. Wang, and X. Yao. To adapt or not to adapt?: Technical debt and learning driven self-adaptation for managing runtime performance. In Proceedings of ACM/SPEC ICPE 2018: 48--55, New York, USA, 2018, ACM.

Digital Library

[9]

S. Cheng and D. Garlan. Stitch: A language for architecture-based self-adaptation. Journal of Systems and Software, 85(12):2860--2875, 2012.

Digital Library

[10]

M. J. V. D. Donckt, D. Weyns, M. U. Iftikhar, and R. K. Singh. Cost-benefit analysis at runtime for self-adaptive systems applied to an internet of things application. In Proc. of the 13th Int. Conf. on Evaluation of Novel Approaches to Software Engineering, ENASE 2018, pages 478--490, 2018, SciTePress.

Digital Library

[11]

C. Hwang and K. Yoon. Multiple Criteria Decision Making, Lecture Notes in Economics and Mathematical Systems. Springer, 1981.

[12]

J. O. Kephart and D. M. Chess. The vision of autonomic computing. IEEE Computer, 36(1):41--50, 2003.

Digital Library

[13]

R. Mirandola, P. Potena, and P. Scandurra. Adaptation space exploration for service-oriented applications. Sci. Comput. Program., 80:356--384, 2014.

[14]

The MSL language. https://github.com/fmselab/msl, 2018.

[15]

L. Rosa, L. Rodrigues, A. Lopes, M. Hiltunen, and R. Schlichting. Self-management of adaptable component-based applications. IEEE Transactions on Software Engineering, 39(3):403--421, March 2013.

Digital Library

[16]

G. Su et al. An iterative decision-making scheme for markov decision processes and its application to self-adaptive systems. In FASE 2016 Proceedings, LNCS 9633, pages 269--286, 2016, Springer, Berlin, Heidelberg.

Digital Library

[17]

D. Weyns, B. R. Schmerl, V. Grassi, S. Malek, R. Mirandola, C. Prehofer, J. Wuttke, J. Andersson, H. Giese, and K. M. Göschka. Software Engineering for Self-Adaptive Systems II: Int. Seminar, Dagstuhl, Germany, chapter On Patterns for Decentralized Control in Self-Adaptive Systems, pages 76--107. Springer, 2013.

Cited By

Riegler MSametinger JVierhauser MWimmer M(2023)A model-based mode-switching framework based on security vulnerability scoresJournal of Systems and Software10.1016/j.jss.2023.111633200:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111633
Matthé MDwyer M(2022)Applying reconfiguration cost and control pattern modeling to self-adaptive systemsProceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings10.1145/3510454.3517056(248-250)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510454.3517056
Matthe M(2022)Applying Reconfiguration Cost and Control Pattern Modeling to Self-Adaptive Systems2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)10.1109/ICSE-Companion55297.2022.9793809(248-250)Online publication date: May-2022
https://doi.org/10.1109/ICSE-Companion55297.2022.9793809

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Information & Contributors

Information

Published In

cover image ACM Other conferences

ECSA '19: Proceedings of the 13th European Conference on Software Architecture - Volume 2

September 2019

286 pages

ISBN:9781450371421

DOI:10.1145/3344948

Editors:
Laurence Duchien
University of Lille, France
,
Catia Trubiani
Gran Sasso Science Institute (GSSI), Italy
,
Riccardo Scandariato
Chalmers | University of Gothenburg, Sweden
,
Raffaela Mirandola
Polytecnico di Milano, Italy
,
Elena Maria Navarro Martinez
Universidad de Castilla-La Mancha, Spain
,
Danny Weyns
Katholieke Universiteit Leuven, Belgium
,
Anne Koziolek
Karlsruhe Institute of Technology, Germany
,
Patrizia Scandurra
University of Bergamo, Italy
,
Clément Quinton
University of Lille, France

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2019

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Swedish KK-Stiftelsens project

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ECSA

ECSA: European Conference on Software Architecture

September 9 - 13, 2019

Paris, France

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ECSA '19 Paper Acceptance Rate 48 of 72 submissions, 67%;

Overall Acceptance Rate 48 of 72 submissions, 67%

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Cited By

Riegler MSametinger JVierhauser MWimmer M(2023)A model-based mode-switching framework based on security vulnerability scoresJournal of Systems and Software10.1016/j.jss.2023.111633200:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111633
Matthé MDwyer M(2022)Applying reconfiguration cost and control pattern modeling to self-adaptive systemsProceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings10.1145/3510454.3517056(248-250)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510454.3517056
Matthe M(2022)Applying Reconfiguration Cost and Control Pattern Modeling to Self-Adaptive Systems2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)10.1109/ICSE-Companion55297.2022.9793809(248-250)Online publication date: May-2022
https://doi.org/10.1109/ICSE-Companion55297.2022.9793809

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