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

Hans Indrawan Sucipto,

Gregorius Natanael Elwirehardja, Nicholas Dominic

et al.

Information, Journal Year: 2025, Volume and Issue: 16(3), P. 213 - 213

Published: March 10, 2025

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

Language: Английский

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

Hans Indrawan Sucipto,

Gregorius Natanael Elwirehardja, Nicholas Dominic

et al.

Information, Journal Year: 2025, Volume and Issue: 16(3), P. 213 - 213

Published: March 10, 2025

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

Language: Английский

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