Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option to reconfigure by analyzing the possible adaptation options, i.e., the adaptation space. Yet, analyzing large adaptation spaces using rigorous methods can be resource- and time-consuming, or even be infeasible. One approach to tackle this problem is by using online machine learning to reduce adaptation spaces. However, existing approaches require domain expertise to perform feature engineering to define the learner, and support online adaptation space reduction only for specific goals. To tackle these limitations, we present ”Deep Learning for Adaptation Space Reduction Plus” – DLASeR+ in short. 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. We evaluate DLASeR+ on two instances of an Internet-of-Things application with increasing sizes of adaptation spaces for different combinations of adaptation goals. We compare DLASeR+ with a baseline that applies exhaustive analysis and two state-of-the-art approaches for adaptation space reduction that rely on learning. Results show that DLASeR+ is effective with a negligible effect on the realization of the adaptation goals compared to an exhaustive analysis approach, and supports three common types of adaptation goals beyond the state-of-the-art approaches.

当今许多软件系统都面临着不确定的操作条件，例如资源可用性的突然变化或意外的用户行为。如果没有适当的缓解措施，这些不确定性可能会危及系统目标。自适应是解决此类不确定性的常用方法。当系统目标可能受到损害时，自适应系统必须通过分析可能的适应选项即适应空间来选择最佳适应选项进行重新配置。然而，使用严格的方法分析大型适应空间可能会耗费资源和时间，甚至是不可行的。解决这个问题的一种方法是使用在线机器学习来减少适应空间。但是，现有方法需要领域专业知识执行特征工程来定义学习者，并支持仅针对特定目标的在线适应空间缩减。为了解决这些限制，我们提出了“深度学习用于适应空间减少加”——简称 DLASeR+。DLASeR+ 提供了一个可扩展的在线适应空间缩减学习框架，不需要特征工程，同时支持三种常见类型的适应目标：阈值、优化和设定点目标。我们在物联网应用程序的两个实例上评估 DLASeR+，随着适应空间大小的增加，适应目标的不同组合。我们将 DLASeR+ 与一个基线进行了比较，该基线应用了详尽的分析和两种最先进的依赖于学习的适应空间缩减方法。结果表明，与详尽的分析方法相比，DLASeR+ 是有效的，对实现适应目标的影响可以忽略不计，