Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this article, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space, we refer to the set of adaptation options a self-adaptive system can select from to adapt at a given time based on the estimated quality properties of the adaptation options. A drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that the quality of the system may deteriorate, eventually, no adaptation option may satisfy the initial set of adaptation goals, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such a shift corresponds to a novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation. The lifelong ML layer tracks the system and its environment, associates this knowledge with the current learning tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. 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. We validate the approach for a series of scenarios with a drift of adaptation spaces using the DeltaIoT exemplar.

最近，机器学习（ML）已成为支持自适应的流行方法。机器学习已被用来处理自适应中的几个问题，例如在不确定性和可扩展决策下维持最新的运行时模型。然而，利用机器学习会带来固有的挑战。在本文中，我们重点关注基于学习的自适应系统的一个特别重要的挑战：适应空间的漂移。对于适应空间，我们指的是自适应系统可以根据适应选项的估计质量属性在给定时间进行选择的一组适应选项。适应空间的漂移源于不确定性，影响适应选项的质量属性。这种漂移可能意味着系统的质量可能恶化，最终没有任何适应选项可以满足初始的一组适应目标，或者可能出现允许增强适应目标的适应选项。在机器学习中，这种转变对应于一种新颖的类外观，即目标数据中的一种概念漂移，常见的机器学习技术在处理时存在问题。为了解决这个问题，我们提出了一种新颖的自适应方法，通过终身机器学习层增强基于学习的自适应系统。我们将这种方法称为终身自适应。终身机器学习层跟踪系统及其环境，将这些知识与当前的学习任务相关联，根据差异识别新任务，并相应地更新自适应系统的学习模型。人类利益相关者可能会参与支持学习过程并调整学习和目标模型。我们提出了一种终身自适应的通用架构，并将其应用于影响自适应决策的适应空间漂移的情况。我们使用 DeltaIoT 示例验证了一系列具有适应空间漂移的场景的方法。