Lessons learned from hyper-parameter tuning for microservice candidate identification
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.
Wed 17 NovDisplayed time zone: Hobart change
11:00 - 12:00
|Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings|
|Lessons learned from hyper-parameter tuning for microservice candidate identification|
|BeeSwarm: Enabling Parallel Scaling Performance Measurement in Continuous Integration for HPC Applications|
Jacob Tronge Kent State University, qiang guan Kent State University, Jieyang Chen , Patricia Grubel Los Alamos National Laboratory, Tim Randles Los Alamos National Laboratory, Rusty Davis Los Alamos National Laboratory, Quincy Wofford Los Alamos National Laboratory, Steven Anaya Los Alamos National Laboratory