Analysis of microservices-based IoT systems: deployment challenges, industry practices, and performance insights

As the adoption of microservices in Internet of Things (IoT) systems grows, deploying them on the Edge remains a significant challenge for practitioners. While Edge Computing offers improved latency and resource efficiency by processing data near the source, it also introduces complexity in managing microservices. Despite increasing academic interest, few comprehensive studies have investigated the specific challenges and effective software engineering (SE) practices for deploying microservices-based IoT systems on the Edge. Therefore, we conducted a multi-method study to bridge this gap. We used three methods: (1) a systematic literature review (SLR) to identify known challenges and approaches, (2) a gray literature review (GLR) to extract SE practices used in the field, and (3) an empirical evaluation using two versions of a case study, one with and one without selected SE practices. The findings show that (1) the most reported challenges relate to resource utilization and performance optimization, (2) containerized microservices, API gateways, and database-per-service are among the most commonly recommended practices, and (3) implementing these practices led to a 132% throughput improvement, 49% reduction in latency, and memory savings of up to 13% in Edge-based IoT systems. However, increased architectural complexity also led to higher CPU usage. This study offers a catalog of best practices and empirical evidence to support IoT developers aiming to optimize microservices-based deployments on the Edge, particularly in resource-constrained environments.