Cloud-edge MQTT messaging for latency mitigation and broker memory footprint reduction.

Abstract

The deployment of smart-city applications has increased the number of Internet of Things (IoT) devices connected to a network cloud. Thanks to its flexibility in matching data publishers and subscribers, broker-based data communication could be a solution for such IoT data delivery, and MQTT is one of the widely used messaging protocols in this class. While MQTT by default does not differentiate message flows by size, it is observed that transient local network congestion may cause size-dependent latency additions, and that the accumulation of large message copies in the cloud broker could run out of the broker memory. In response, in the scope of cloud-edge messaging, this research article presents problem analysis, system design and implementation, and empirical and analytical performance evaluation. The article introduces three message scheduling policies for subscribers deployed at network edge, and a memory allocation scheme for MQTT broker deployed at network cloud. The proposed design has been implemented based on Eclipse Mosquitto, an open-source MQTT broker implementation. Empirical and analytical validations have demonstrated the performance of the proposed design in latency mitigation, and the result also shows that, empirically, the proposed design may save the run-time broker memory footprint by about 75%. Applicability of the proposed design to other messaging services are discussed by the end of the article.