Self-adaptive system (SAS) can adapt itself to optimize various key performance indicators in response to the dynamics and uncertainty in environment. In this paper, we present Debt Learning Driven Adaptation (DLDA), an framework that dynamically determines when and whether to adapt the SAS at runtime. DLDA leverages the temporal adaptation debt, a notion derived from the technical debt metaphor, to quantify the time-varying money that the SAS carries in relation to its performance and Service Level Agreements. We designed a temporal net debt driven labeling to label whether it is economically healthier to adapt the SAS (or not) in a circumstance, based on which an online machine learning classifier learns the correlation, and then predicts whether to adapt under the future circumstances. We conducted comprehensive experiments to evaluate DLDA with two different planners, using 5 online machine learning classifiers, and in comparison to 4 state-of-the-art debtoblivious triggering approaches. The results reveal the effectiveness and superiority of DLDA according to different metrics.
For example, Chen et al. REF have used technical debt as a metaphor to model the problem of to adapt or not to adapt.
To Adapt or Not to Adapt?: Technical Debt and Learning Driven Self-Adaptation for Managing Runtime Performance
ICPE '18
Computer Science Engineering