Result Details
KuBench: A Kubernetes-Based Environment for Standardized REST API Framework Performance Evaluation
Šauer Matěj, Bc., DCSY (FIT)
Jaroš Marta, Ing., Ph.D., DCSY (FIT)
Jaroš Jiří, prof. Ing., Ph.D., DCSY (FIT)
Selecting the optimal REST API framework is a critical decision that directly impacts application performance, scalability, and development efficiency. KuBench solves this challenge by providing an environment for comparison of multiple REST API frameworks. Unlike existing tools that only test single implementations, our Kubernetes-based solution enables developers to evaluate multiple implementations of identical APIs across different programming languages and libraries under the same conditions. This paper presents our approach for consistent benchmarking using containerization and comprehensive performance metrics that capture response times, throughput, and resource utilization. KuBench empowers development teams to make decisions when selecting REST API frameworks that genuinely meet their performance requirements.
REST API; benchmarking; performance testing; resource utilization
@inproceedings{BUT197687,
author="Ondřej {Olšák} and Matěj {Šauer} and Marta {Jaroš} and Jiří {Jaroš}",
title="KuBench: A Kubernetes-Based Environment for Standardized REST API Framework Performance Evaluation",
booktitle="Lecture Notes in Computer Science",
year="2025",
pages="366--369",
publisher="Springer Nature Switzerland",
address="Cham",
doi="10.1007/978-3-031-97207-2\{_}28",
isbn="978-3-031-97206-5",
url="https://link.springer.com/chapter/10.1007/978-3-031-97207-2_28"
}
Closed-loop Individualized image-guided Transcranial Ultrasonic Stimulation, EU, HORIZON EUROPE, 101071008, start: 2022-08-01, end: 2026-07-31, running