Kubernetes-Powered Cardiovascular Monitoring: Enhancing Internet of Things Heart Rate Systems for Scalability and Efficiency

Abstract

Reliable system design is an important component to ensure data processing speed, service availability, and an improved user experience. Several studies have been conducted to provide data processing speeds for health monitors using clouds or edge devices. However, if the system design used cannot handle many requests, the reliability of the monitoring itself will be reduced. This study used the Kubernetes approach for system design, leveraging its scalability and efficient resource management. The system was deployed in a local Kubernetes environment using an Intel Xeon CPU E5-1620 with 8 GB RAM. This study compared two architectures: MQTT (traditional method) and MQTT-Kafka (proposed method). The proposed method shows a significant improvement, such as throughput results on the proposed method of 1587 packets/s rather than the traditional methods at 484 packets/s. The response time and latency are 95% more stable than the traditional method, and the performance of the proposed method also requires a larger resource of approximately 30% more than the traditional method. The performance of the proposed method requires the use of a large amount of RAM for a resource-limited environment, with the highest RAM usage at 5.63 Gb, while the traditional method requires 4.5 Gb for the highest RAM requirement.