Feature-Model-Guided Online Learning for Self-Adaptive Systems

Metzger, Andreas; Quinton, Clément; Mann, Zoltán Ádám; Baresi, Luciano; Pohl, Klaus

doi:10.48550/arxiv.1907.09158

Cited by 3 publications

(3 citation statements)

References 66 publications

(136 reference statements)

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“…Metzger et al [49] apply online learning to explore the adaptation space of self-adaptive systems using feature models. The authors demonstrate a speedup in convergence of the online learning process.…”

Section: Learning Used For Adaptation Space Reductionmentioning

confidence: 99%

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems

Weyns

Gheibi

Quin

et al. 2022

ACM Trans. Auton. Adapt. Syst.

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Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option to reconfigure by analyzing the possible adaptation options, i.e., the adaptation space. Yet, analyzing large adaptation spaces using rigorous methods can be resource- and time-consuming, or even be infeasible. One approach to tackle this problem is by using online machine learning to reduce adaptation spaces. However, existing approaches require domain expertise to perform feature engineering to define the learner, and support online adaptation space reduction only for specific goals. To tackle these limitations, we present ”Deep Learning for Adaptation Space Reduction Plus” – DLASeR+ in short. DLASeR+ offers an extendable learning framework for online adaptation space reduction that does not require feature engineering, while supporting three common types of adaptation goals: threshold, optimization, and set-point goals. We evaluate DLASeR+ on two instances of an Internet-of-Things application with increasing sizes of adaptation spaces for different combinations of adaptation goals. We compare DLASeR+ with a baseline that applies exhaustive analysis and two state-of-the-art approaches for adaptation space reduction that rely on learning. Results show that DLASeR+ is effective with a negligible effect on the realization of the adaptation goals compared to an exhaustive analysis approach, and supports three common types of adaptation goals beyond the state-of-the-art approaches.

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Section: Learning Used For Adaptation Space Reductionmentioning

confidence: 99%

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems

Weyns

Gheibi

Quin

et al. 2022

ACM Trans. Auton. Adapt. Syst.

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“…As the complexity of selfadaptive software has steadily grown over time, the opportunities to exploit ML to support MAPE have also increased. Just as ML has found substantial success in recent years in the perception pipeline of automotive vehicles, such techniques have been deployed to monitor computing systems at runtime to derive meaningful measures from data gathered in complex environments [23] and to detect faults [24]. During the analysis phase, ML has been used when the space of possible adaptations grows too large to be handled with traditional techniques [25] or where patterns may be extracted from large data sets (e.g., network traffic [26]).…”

Section: Related Workmentioning

confidence: 99%

Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

Weyns

Schmerl

Kishida

et al. 2021

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.

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“…An adaptation option is labeled relevant when it is predicted to satisfy the adaptation goals. Techniques have been applied in this approach including search-based techniques [53] and feature-based techniques [49]. Recently, different machine learning techniques have been investigated to reduce the adaptation space at runtime, see for instance [22,37,58].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems

Weyns¹,

Gheibi²,

Quin³

et al. 2022

Preprint

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Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option to reconfigure by analyzing the possible adaptation options, i.e., the adaptation space. Yet, analyzing large adaptation spaces using rigorous methods can be resource-and time-consuming, or even be infeasible. One approach to tackle this problem is by using online machine learning to reduce adaptation spaces. However, existing approaches require domain expertise to perform feature engineering to define the learner, and support online adaptation space reduction only for specific goals. To tackle these limitations, we present "Deep Learning for Adaptation Space Reduction Plus" -DLASeR+ in short. DLASeR+ offers an extendable learning framework for online adaptation space reduction that does not require feature engineering, while supporting three common types of adaptation goals: threshold, optimization, and set-point goals. We evaluate DLASeR+ on two instances of an Internet-of-Things application with increasing sizes of adaptation spaces for different combinations of adaptation goals. We compare DLASeR+ with a baseline that applies exhaustive analysis and two stateof-the-art approaches for adaptation space reduction that rely on learning. Results show that DLASeR+ is effective with a negligible effect on the realization of the adaptation goals compared to an exhaustive analysis approach, and supports three common types of adaptation goals beyond the state-of-the-art approaches.CCS Concepts: • Software and its engineering → Software design engineering; • Theory of computation → Online learning algorithms.

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Feature-Model-Guided Online Learning for Self-Adaptive Systems

Cited by 3 publications

References 66 publications

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems

Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems

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