Performance Comparison of AWS Lambda and Azure Functions Using Spring Boot and Quarkus Java Framework

Gaikwad, Siddhesh Manik (2025) Performance Comparison of AWS Lambda and Azure Functions Using Spring Boot and Quarkus Java Framework. Masters thesis, Dublin, National College of Ireland.

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Abstract

The results in question can be regarded only as preliminary for the small number of subjects included. Serverless computing (Function-as-a-Service) has become a major paradigm for building cloud-native applications. Amazon Web Services (AWS) Lambda and Microsoft Azure Functions are among the lead providers of such platforms. However, using older Java programming models (e.g., Spring Boot) with these cloud computing platforms can create challenges, most notably cold start latency. Developers have developed modern programming models (e.g., Quarkus) that take advantage of optimizations aimed at reducing cold starts (initial invocation latencies) by optimizing for quick start-up and low memory usage. This research compares the performance of four different implementations of Java for serverless computing on both AWS Lambda and Azure Function: Spring Boot and Quarkus. A test function was created and deployed to AWS Lambda, Azure Functions, via Cloud Functions. The cold-start and warm request latencies were measured during performance testing. In the initial test of Spring Boot running on AWS Lambda, 655 ms was the fastest recorded cold start time, whereas the average warm response time for Spring Boot running on Azure Functions was 739.50 ms. Preliminary results show trade-offs across platforms and frameworks. These results demonstrate the complexity of the relationship between the framework's optimization features and the environment's execution runtime platform. The empirical data provided in this study can serve as a basis for determining the technology (i.e., framework and platform) for building Java-based serverless applications.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Mijumbi, Rashid UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	31 Aug 2026 14:19
Last Modified:	31 Aug 2026 14:19
URI:	https://norma.ncirl.ie/id/eprint/9701

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