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

Chen，Tao1; Bahsoon，Rami2; Wang，Shuo2; Yao，Xin3

10.1145/3184407.3184413

2018-03-30

ICPE 2018 - Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering

2018-March

48-55

Merida, Mexico

Association for Computing Machinery, Inc

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.

Learning Performance Self-adaptive systems Technical debt

其他

英语

EI

Engineering and Physical Sciences Research Council[EP/J017515/1]

20183205668270

Adaptive systems ; Artificial intelligence ; Benchmarking ; Learning systems ; Outsourcing ; Software engineering

Computer Programming:723.1 ; Artificial Intelligence:723.4 ; Management:912.2

2-s2.0-85051087850

Scopus

1.Department of Computing and Technology, Nottingham Trent University, ,United Kingdom
2.CERCIA, School of Computer Science, University of Birmingham, ,United Kingdom
3.Department of Computer Science and Engineering, Southern University of Science and Technology, ,China

Chen，Tao,Bahsoon，Rami,Wang，Shuo,et al. To adapt or not to adapt? Technical debt and learning driven self-adaptation for managing runtime performance[C]:Association for Computing Machinery, Inc,2018:48-55.