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Comparing model-based predictive approaches to self-adaptation: CobRA and PLA

Authors:

Gabriel A. Moreno,

Alessandro V. Papadopoulos,

Konstantinos Angelopoulos,

Javier Cámara,

Bradley SchmerlAuthors Info & Claims

SEAMS '17: Proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

Pages 42 - 53

https://doi.org/10.1109/SEAMS.2017.2

Published: 20 May 2017 Publication History

Abstract

Modern software-intensive systems must often guarantee certain quality requirements under changing run-time conditions and high levels of uncertainty. Self-adaptation has proven to be an effective way to engineer systems that can address such challenges, but many of these approaches are purely reactive and adapt only after a failure has taken place. To overcome some of the limitations of reactive approaches (e.g., lagging behind environment changes and favoring short-term improvements), recent proactive self-adaptation mechanisms apply ideas from control theory, such as model predictive control (MPC), to improve adaptation. When selecting which MPC approach to apply, the improvement that can be obtained with each approach is scenario-dependent, and so guidance is needed to better understand how to choose an approach for a given situation. In this paper, we compare CobRA and PLA, two approaches that are inspired by MPC. CobRA is a requirements-based approach that applies control theory, whereas PLA is architecture-based and applies stochastic analysis. We compare the two approaches applied to RUBiS, a benchmark system for web and cloud application performance, discussing the required expertise needed to use both approaches and comparing their run-time performance with respect to different metrics.

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Comparing model-based predictive approaches to self-adaptation: CobRA and PLA
1. General and reference
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Published In

cover image ACM Conferences

SEAMS '17: Proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

May 2017

227 pages

ISBN:9781538615508

General Chair:
David Garlan
Carnegie Mellon University
,
Program Chair:
Bashar Nuseibeh
The Open University, and Lero

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SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society
SADIO: SADIO

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IEEE Press

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Published: 20 May 2017

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ICSE '17: 39th International Conference on Software Engineering

May 20 - 28, 2017

Buenos Aires, Argentina

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Overall Acceptance Rate 17 of 31 submissions, 55%

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Cited By

Metzger ALaufer JFeit FPohl K(2024)A User Study on Explainable Online Reinforcement Learning for Adaptive SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/366600519:3(1-44)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3666005
Qin YTong YXu YCao CMa X(2024)Active Monitoring Mechanism for Control-Based Self-Adaptive SystemsProceedings of the ACM on Software Engineering10.1145/36607891:FSE(1841-1864)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660789
Vilchez ETroya JCamara JBaresi LMa XPasquale L(2024)Towards Proactive Decentralized Adaptation of Unmanned Aerial Vehicles for Wildfire TrackingProceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/3643915.3644081(56-62)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643915.3644081
Chen ZLi JLi NJiao W(2024)Reliable proactive adaptation via prediction fusion and extended stochastic model predictive controlJournal of Systems and Software10.1016/j.jss.2024.112166217:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112166
Fang XYaman SCalinescu RWilson JPaterson C(2023)Predicting Nonfunctional Requirement Violations in Autonomous SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/363240519:1(1-25)Online publication date: 14-Nov-2023
https://dl.acm.org/doi/10.1145/3632405
Haq ADeshpande NElSaid ADesell TKrutz DWagner M(2022)Addressing tactic volatility in self-adaptive systems using evolved recurrent neural networks and uncertainty reduction tacticsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528745(1299-1307)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528745
Fan JTong YQin YMa X(2020)Overwhelming Uncertainty in Self-adaptation: An Empirical Study on PLA and CobRAProceedings of the 12th Asia-Pacific Symposium on Internetware10.1145/3457913.3457943(250-259)Online publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1145/3457913.3457943
Marron ALimonad LPollack SHarel DHoniden SNitto ECalinescu R(2020)Expecting the unexpectedProceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/3387939.3391607(167-173)Online publication date: 29-Jun-2020
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Mandrioli CMaggio MDevanbu PCohen MZimmermann T(2020)Testing self-adaptive software with probabilistic guarantees on performance metricsProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409685(1002-1014)Online publication date: 8-Nov-2020
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Lukina ATiwari ASmolka SGrosu RHung CPapadopoulos G(2019)Distributed adaptive-neighborhood control for stochastic reachability in multi-agent systemsProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297370(914-921)Online publication date: 8-Apr-2019
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