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：近期，机器学习已成为支持自适应系统的主流方法，被广泛用于解决自适应系统中的诸多问题，例如在不确定性条件下维护实时运行时模型以及实现可扩展决策制定。然而，利用机器学习也伴随着固有挑战。本文聚焦于学习型自适应系统面临的关键挑战：适应空间漂移。所谓适应空间，是指自适应系统在特定时刻根据各适应选项的预估质量属性进行选择的可选方案集合。适应空间漂移源于不确定性对适应选项质量属性的影响，可能导致系统始终无法找到满足初始适应目标集的有效方案，从而降低系统质量；也可能催生能够增强适应目标的新方案。在机器学习领域，这种漂移对应目标数据中新型类别的出现，属于传统机器学习技术难以处理的概念漂移类型。为解决该问题，我们提出新型自适应方法，通过引入终身机器学习层增强学习型自适应系统，将其命名为"终身自适应"。该终身机器学习层持续追踪系统及其环境，将动态知识与当前任务关联，通过差异识别新任务，并据此更新自适应系统的学习模型。同时可引入人类利益相关者辅助学习过程，调整学习模型与目标模型。我们提出了终身自适应的通用架构，并针对影响自适应决策制定的适应空间漂移场景进行应用验证。最终通过DeltaIoT案例系列实验对方法有效性进行验证。