Optimizing microservices with hyperparameter optimization

TL;DR

This paper proposes a method for optimizing microservices performance using grid and random search techniques, achieving up to 10.56% latency improvements in operational settings.

Contribution

It introduces a novel self-optimization approach for microservices based on hyperparameter search methods, addressing operational challenges.

Findings

Latency performance improved by up to 10.56%

Effective use of grid and random search techniques

Demonstrated feasibility of self-optimizing microservices

Abstract

In the last few years, the cloudification of applications requires new concepts and techniques to fully reap the benefits of the new computing paradigm. Among them, the microservices architectural style, which is inspired by service-oriented architectures, has gained attention from both industry and academia. However, decomposing a monolith into multiple microservices also creates several challenges across the application's lifecycle. In this work, we focus on the operation aspect of microservices, and present our novel proposal to enable self-optimizing microservices systems based on grid search and random search techniques. The initial results show our approach is able to optimize the latency performance of microservices to up to 10.56\%.