Aquisição de Dados Escalável e Ciente da Aplicação para Gêmeos Digitais
Abstract
Digital Twin is the name given to a type of system that has full integration between a physical part and a digital part updated in real time. In general, they are large-scale systems with intense flow of data between their parts (physical and digital). To perform data acquisition in this type of system, protocols such as MQTT are commonly used. This work recognizes the limitations of this protocol when using high traffic conditions and explores alternatives to increase data flow while minimizing message loss and resource usage. For this, the native cluster mode of MQTT broker implementations is used, adding a component to the system architecture which we call “Cluster Manager”. This component was developed with the objective of monitoring the health of a cluster of brokers and adjusting the composition of the cluster to the message flow. The results of the implementation of this component helped to determine loss margins and latencies in sending critical messages, using different strategies to scale the cluster under representative workloads of Digital Twins applications. It has also been determined that using very sensitive parameters to scale the system causes more nodes than necessary to be instantiated and resources wasted.
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