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](https://github.com/yrahul3910/ase-tuned-mono2micro) for a reproduction package for this research.
@article{arxiv.2106.06652,
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},
journal= {arXiv preprint arXiv:2106.06652},
year = {2021}
}
Comments
Accepted to ASE 2021 (industry track, short paper)