Fossa: Learning ECA Rules for Adaptive Distributed Systems

“…The diagram shows that in a substantial number of papers (34 in total), learning supports both analysis and planning. An example is described in [32], where machine learning is applied to generate Event-Condition-Action rules that are evaluated and subsequently used to make adaptation decisions. A smaller fraction of the papers (6 in total) combine learning to support monitoring and analysis.…”

Section: Mape Functions Supported By Learningmentioning

confidence: 99%

Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Gheibi,

Weyns,

Quin

2021

Preprint

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Recently, we witness a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analysing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such overview is important for researchers to understand the state of the art and direct future research efforts. This paper reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional MAPE-based feedback loop (Monitor-Analyze-Plan-Execute). The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

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“…The diagram shows that in a substantial number of papers (34 in total), learning supports both analysis and planning. An example is described in Reference [33], where machine learning is applied to generate Event-Condition-Action rules that are evaluated and subsequently used to make adaptation decisions. A smaller fraction of the papers (six in total) combine learning to support monitoring and analysis.…”

Section: Rq2: What Are the Key Engineering Aspects Considered When Applying Learning In Self-adaptation?mentioning

confidence: 99%

“…Moreover, to mitigate this threat, we excluded all short papers and papers that do not provide a minimum level of assessment. For instance, we excluded Reference [33], although the topic is relevant for our study, but this is a short paper. Similarly, we excluded Reference [81], since this regular paper does not provide a sufficient level of assessment.…”

Section: Threats To Validitymentioning

confidence: 99%

Applying Machine Learning in Self-adaptive Systems

Gheibi

Weyns

Quin

2020

ACM Trans. Auton. Adapt. Syst.

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Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

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Fossa: Learning ECA Rules for Adaptive Distributed Systems

Cited by 20 publications

References 18 publications

RADAR: Data Protection in Cloud-Based Computer Systems at Run Time

RADAR: Data Protection in Cloud-Based Computer Systems at Run Time

Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Applying Machine Learning in Self-adaptive Systems

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