一句话：揭示微服务拆分工具评估常忽略超参数优化，并证明调优能显著提升分区效果。
将单体应用拆分为微服务需要工具支持，但已有方法的评估常不完整，尤其缺少对超参数优化的探索。论文使用一组开源 Java EE 应用，系统研究超参数调优对微服务候选识别与分区质量的影响。结果显示，超参数优化能显著改善微服务分区；同时，如何针对不同问题选择最佳优化器仍是未来工作的开放问题。作者提供复现包，提醒研究者在评估微服务拆分方法时不应忽视超参数调优的作用。
When optimizing software for the cloud, monolithic applications need to be partitioned into many smaller microservices. While many tools have been proposed for this task, we warn that the evaluation of those approaches has been incomplete; e.g. minimal prior exploration of hyperparameter optimization. Using a set of open source Java EE applications, we show here that (a) such optimization can significantly improve microservice partitioning; and that (b) an open issue for future work is how to find which optimizer works best for different problems. To facilitate that future work, see https://github.com/yrahul3910/ase-tuned-mono2micro for a reproduction package for this research.
DOI 原文 ·
@inproceedings{paperbot6546,
title = {Lessons learned from hyper-parameter tuning for microservice candidate identification},
author = {Rahul Yedida and Rahul Krishna and Anup Kalia and Tim Menzies and Jin Xiao and Maja Vukovic},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678704}
}