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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 19.09.2019, 15:19 by Tao Chen, Rami Bahsoon, Shuo Wang, Xin Yao
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.

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 - 55

Source

2018 ACM/SPEC International Conference on Performance Engineering

Publisher

Association for Computing Machinery (ACM)

Version

VoR (Version of Record)

Rights holder

© Association for Computing Machinery

Publication date

2018-03-30

Copyright date

2018

ISBN

9781450350952

Language

en

Location

Berlin, Germany

Event dates

9th April 2018 - 13th April 2018

Depositor

Dr Tao Chen

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