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 debt-oblivious triggering approaches. The results reveal the effectiveness and superiority of DLDA according to different metrics. © 2018 Association for Computing Machinery.
|Title of host publication
|ICPE 2018 - Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering
|Association for Computing Machinery, Inc
|Number of pages
|Published - 30 Mar 2018
Bibliographical noteThis work is supported by the DAASE Programme Grant from the EPSRC (Grant No. EP/J017515/1).
- Self-adaptive systems
- Technical debt