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 paper, 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 at a given time to adapt based on the estimated quality properties of the adaptation options. Drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that eventually no adaptation option can satisfy the initial set of the adaptation goals, deteriorating the quality of the system, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such shift corresponds to 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 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 using the DeltaIoT exemplar.

翻译：近年来，机器学习已成为支持自适应系统的主流方法，被用于解决自适应中的诸多难题，例如在不确定性条件下维护运行时模型的时效性以及实现可扩展的决策。然而，应用机器学习也伴随固有挑战。本文聚焦于基于学习的自适应系统中一项关键挑战：适应空间的漂移。适应空间指自适应系统在特定时刻可选择的适应选项集合，其选择基于对适应选项质量属性的评估。适应空间的漂移源于不确定性对适应选项质量属性的影响：此类漂移可能导致最终无适应选项能满足初始适应目标，从而降低系统质量；亦可能催生新的适应选项，促使适应目标的提升。在机器学习领域，这种偏移对应目标数据中概念漂移的变体——新类别的出现，而常规机器学习技术难以应对该问题。为攻克这一难题，我们提出一种新型自适应方法，通过在基于学习的自适应系统中引入终身机器学习层，将其增强为"终身自适应"系统。该终身机器学习层持续监测系统及其环境，将监测数据与当前任务关联，识别差异中新任务，并据此更新自适应系统的学习模型。人类利益相关者可参与支持学习过程，协同调整学习模型与目标模型。我们设计了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场景。最后，基于DeltaIoT实例在多组实验场景中验证了该方法的有效性。