Performance evaluation of serverless cloud-native API deployment: a case study on a mobile health application

Abstract

As software applications become increasingly complex, there is a growing need for scalable, flexible, and high-performance backend solutions. Cloud computing-based application programming interfaces (APIs) address these demands by enabling developers to offload resource-intensive tasks to the cloud while eliminating the burden of infrastructure management. This study presents a case study using Obesifix, a mobile health application for real time dietary monitoring and personalized nutrition recommendations. Two deployment models were evaluated: a traditional server-based architecture using Google Compute Engine (GCE) and a serverless approach using Google Cloud Run (GCR). Performance testing was conducted with Apache JMeter under simulated loads of 60, 120, and 180 users across four critical API endpoints (register, login, recommendation, prediction). Results show that GCR consistently achieved 20–30% lower response times and 15–20% higher throughput compared to GCE, while maintaining 0% error rate, lower memory consumption, and more balanced virtual central processing unit (vCPU) utilization. Time to first byte (TTFB) remained below 800 ms across all scenarios, confirming good server responsiveness. These findings highlight the scalability and efficiency benefits of serverless architectures for mobile health applications. Future research should explore asynchronous programming paradigms, autoscaling thresholds, and cost-performance trade-offs, as well as multi-cloud deployments to enhance system resilience and generalizability.