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Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptation
We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation.
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems
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.
Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning
In this paper, we focus on one such challenge that is particularly important for self-adaptation: ML techniques are designed to deal with a set of predefined tasks associated with an operational domain; they have problems to deal with new emerging tasks, such as concept shift in input data that is used for learning.
On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems
Yet, since machine learning methods apply in essence statistical methods, they may have an impact on the decisions made by a self-adaptive system.
Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review
The research questions are centred 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.
