Vampire: A Smart Energy Meter for Synchronous Monitoring in a Distributed Computer System
Metadatos
Mostrar el registro completo del ítemAutor
Díaz García, Antonio Francisco; Prieto Campos, Beatriz; Escobar Pérez, Juan José; Lampert, ThomasEditorial
Elsevier
Materia
Energy monitoring on distributed computers Smart low-cost wattmeter Open energy meter Smart meter Energy-aware computing
Fecha
2024-02Referencia bibliográfica
Antonio Francisco Díaz, Beatriz Prieto, Juan José Escobar, and Thomas Lampert. 2024. Vampire: A Smart Energy Meter for Synchronous Monitoring in a Distributed Computer System. Journal of Parallel and Distributed Computing 184, (Feb 2024). https://doi.org/10.1016/j.jpdc.2023.104794
Patrocinador
Spanish Ministry of Science, Innovation, and Universities under grants PGC2018–098813-B-C31 and PID2022–137461NB-C31; ERDF fundResumen
This paper presents the design and implementation of a low-cost system oriented to the synchronised and real-time surveillance and monitoring of electrical parameters of different computer devices. To measure energy consumption in a computer system, it is proposed to use, instead of a general-purpose wattmeter, one designed ad-hoc and synchronously collects the energy consumption of its various nodes or devices. The implementation of the devised system is based on the confluence of several technologies or tools widely used in the Internet of Things. Thus, this article the intelligent objects are the power meters, whose connections are based on the low-cost ESP32 microcontroller. The message transmission between devices is carried out with the standard message queuing telemetry transport (MQTT) protocol, the measurements are grouped in a database on an InfluxDB server that store the sensor data as time series, and Grafana is used as a graphical user interface. The efficiency of the proposed energy monitoring system is demonstrated by the experimental results of a real application that successfully and synchronously records the voltage, current, active power and cumulative energy consumption of a distributed cluster that includes a total of 60 cores.