Automatic Adaptation of SOA Systems Supported by Machine Learning

Skałkowski, Kornel; Zieliński, Krzysztof

doi:10.1007/978-3-642-37291-9_7

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…Learning Performance Balance time and accuracy 3 [64,65,82] Handle oscillation in early learning stages 1 [43] Learning Effect…”

Section: Rq3: What Are Open Challenges For Using Machine Learning In ...mentioning

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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“…Learning Performance Balance time and accuracy 3 [64,65,82] Handle oscillation in early learning stages 1 [43] Learning Effect…”

Section: Rq3: What Are Open Challenges For Using Machine Learning In ...mentioning

confidence: 99%

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

Gheibi,

Weyns,

Quin

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Balance time and accuracy 3 [66,67,84] Handle oscillations in early learning stages 1 [45] Learning Effect Understand the effect of learning on adaptation decisions over time 3 [11,72,107] Guarantees on results of machine learning 1 [75] Domain-Related…”

Section: Learning Performancementioning

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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“…EM easily accommodates categorical and continuous data fields making it the most effective technique available for proper probabilistic clustering. Skałkowski and Zieliski [63] recommend the dynamic adaptation of services using machine learning. They show how a clustering algorithm can be used to provide automatic recognition of similar system states and grouping them into subsets (called clusters), based on information provided by the monitoring element interface.…”

Section: Factors That Influence Adaptationmentioning

confidence: 99%

State of runtime adaptation in service‐oriented systems: what, where, when, how and right

Mutanu

Kotonya

2019

IET softw.

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Software as a Service reflects a "service-oriented" approach to software development that is based on the notion of composing applications by discovering and invoking network-available services to accomplish some task. However, as more business organizations adopt service-oriented solutions and the demands on them grow, the problem of ensuring that the software systems can adapt fast and effectively to changing business needs, changes in their runtime environment and failures in provided services has become an increasingly important research problem. Dynamic adaptation has been proposed as a way to address the problem. However, for adaptation to be effective several other factors need to be considered. This paper identifies the key factors that influence runtime adaptation in service-oriented systems, and examines how well they are addressed in 29 adaptation approaches intended to support service-oriented systems.

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Automatic Adaptation of SOA Systems Supported by Machine Learning

Cited by 7 publications

References 11 publications

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

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

Applying Machine Learning in Self-adaptive Systems

State of runtime adaptation in service‐oriented systems: what, where, when, how and right

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