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To adapt or not to adapt? Technical debt and learning driven self-adaptation for managing runtime performance
conference contribution
posted on 2019-09-19, 15:19 authored by Tao Chen, Rami Bahsoon, Shuo Wang, Xin YaoSelf-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.
Funding
DAASE Programme Grant from the EPSRC (Grant No. EP/J017515/1)
History
School
- Science
Department
- Computer Science
Published in
Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering (ICPE '18)Pages
48 - 55Source
2018 ACM/SPEC International Conference on Performance EngineeringPublisher
Association for Computing Machinery (ACM)Version
- VoR (Version of Record)
Rights holder
© Association for Computing MachineryPublication date
2018-03-30Copyright date
2018ISBN
9781450350952Language
- en
Location
Berlin, GermanyEvent dates
9th April 2018 - 13th April 2018Depositor
Dr Tao ChenUsage metrics
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