Lessons learned from hyper-parameter tuning for microservice candidate identification
This addresses a gap in software engineering for cloud optimization, though it is incremental as it builds on existing partitioning methods.
The paper tackles the problem of incomplete evaluation in microservice candidate identification tools by exploring hyperparameter optimization, showing it can significantly improve microservice partitioning on Java EE applications.
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.