Endpoint Monitoring for MQTT Messaging Framework Disconnections
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Solution Overview
Problem
Machine-to-machine (M2M) messaging frameworks face challenges in detecting and addressing high disconnection rates between messaging brokers and MQTT clients, which can lead to user-impacting issues due to the lack of real-time monitoring and historical performance analysis in existing systems.
Innovation Solution
An endpoint monitoring system that queries messaging brokers for disconnection data, compares it with historical data stored in an information data store, and initiates corrective actions when high disconnection rates are detected, alerting engineers to take necessary steps such as rebooting clients or reassigning connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and historical performance analysis are implemented, then disconnection issues can be detected and addressed proactively, but system complexity increases
Solution Approach 1:
A monitoring server acts as an intermediary between messaging brokers and engineers. The server queries brokers for disconnection data, stores historical information in a data store, compares current data against thresholds, and sends alerts to engineers. This intermediary layer enables proactive detection and resolution of disconnection issues without requiring direct complex interactions between brokers and engineering teams.
2Reliability
If disconnection monitoring and alerting systems are implemented, then communication reliability improves, but the system requires additional components and infrastructure
Solution Approach 1:
The monitoring system is segmented into distinct functional components: a monitoring server that queries brokers, an information data store that holds historical disconnection data, and an alerting mechanism that notifies engineers. This segmentation allows each component to perform its specific function independently, making the overall system more manageable and maintainable despite the added complexity.
Data Source
AI summary
An endpoint monitoring for a machine to machine (M2M) messaging framework system is disclosed. The system comprises a monitoring server and a monitoring application stored in a memory of the monitoring server. When executed by a processor of the monitoring server, the monitoring application queries a plurality of brokers that are communicatively coupled with a plurality of message queue telemetry transport (MQTT) clients for data regarding the rate of disconnections that occur between the brokers and the MQTT clients. The monitoring application compares the data received from the brokers and compares the rate of disconnections to data in an information data store. If a high rate of disconnections is discovered in at least one of the broker to MQTT client connections, the monitoring application takes action, for example reboots an MQTT client and/or sends a notification message.