Lessons learned from hyper-parameter tuning for microservice candidate identification

Autor:	Yedida, Rahul, Krishna, Rahul, Kalia, Anup, Menzies, Tim, Xiao, Jin, Vukovic, Maja
Rok vydání:	2021
Předmět:	Computer Science - Software Engineering
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to ASE 2021 (industry track, short paper)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.06652 Zobrazit plný text záznamu View this record from Arxiv