Papers › Lessons learned from hyper-parameter tuning for microservice candidate identification
Lessons learned from hyper-parameter tuning for microservice candidate identification
Rahul Yedida, Rahul Krishna, Anup Kalia, Tim Menzies, Jin Xiao, Maja Vukovic
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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.
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