Enhancing Performance of Cloud-based Software Applications with GraalVM and Quarkus

TL;DR

This paper investigates how GraalVM and Quarkus can improve the performance, scalability, and resource management of cloud-based Java applications deployed on Kubernetes, through native image compilation and optimized frameworks.

Contribution

It introduces the integration of GraalVM and Quarkus for enhanced cloud application performance, focusing on native image creation and deployment in containerized environments.

Findings

GraalVM native images significantly reduce startup time.

Quarkus improves resource efficiency and scalability.

Performance benchmarks outperform traditional Java frameworks.

Abstract

Increased complexity of network-based software solutions and the ever-rising number of concurrent users forced a shift of the IT industry to cloud computing. Conventional network software systems commonly based on monolithic application stack running on costly physical single-purpose servers are affected by significant problems of resource management, computing power distribution, and scalability.Such implementation is restricting applications to be reduced to smaller, independent services that can be more easily deployed, managed, and scaled dynamically; therefore, embellishing environmental uniformity across development, testing, and production. Current cloud-based infrastructure frequently runs on containers placed in Kubernetes or Docker-based cluster, and the system configuration is considerably different compared to the environment prevailed with common virtualizations. This paper discusses the usage of GraalVM, a polyglot high-performance virtual machine for JVM-based and other languages, combined with new Kubernetes native Java tailored stacked framework named Quarkus, formed from enhanced Java libraries. Moreover, our research explores GraalVMs creation of native images using Ahead-Of-Time (AOT) compilation and Quarkus deployment to Kubernetes. Furthermore, we examined the architectures of given systems, various performance variables, and differing memory usage cases within our academic testing environment and presented the comparison results of selected performance measures with other traditional and contemporary solutions