To adapt or not to adapt? Technical debt and learning driven self-adaptation for managing runtime performance

Tools

Chen, T. ORCID: 0000-0001-5025-5472, Bahsoon, R., Wang, S. and Yao, X., 2018. To adapt or not to adapt? Technical debt and learning driven self-adaptation for managing runtime performance. In: Proceedings of ICPE ’18: 9th ACM/SPEC International Conference on Performance Engineering, Berlin, Germany, 9–13 April 2018. New York: Association for Computing Machinery (ACM), pp. 48-55. ISBN 9781450350952

Preview

Text
PubSub10440_809a_Chen.pdf - Post-print
Download (1MB) | Preview

Official URL: http://dx.doi.org/10.1145/3184407.3184413

Abstract

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.

Item Type:

Chapter in book

Creators:

Chen, T., Bahsoon, R., Wang, S. and Yao, X.

Publisher:

Association for Computing Machinery (ACM)

Place of Publication:

New York

Date:

April 2018

ISBN:

9781450350952