Messaging Abstraction Module for Proactive MOM Server Failure Prediction
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Solution Overview
Problem
Existing message-oriented middleware (MOM) systems lack intelligence in predicting server failures and recommending suitable alternate servers, leading to messaging outages and delays in processing mission-critical transactions.
Innovation Solution
A messaging abstraction module that uses machine learning models, such as an isolation forest for anomaly prediction and a decision tree-based algorithm for server recommendation, to identify upcoming outages and route messages to alternate MOM servers, ensuring seamless handling and optimal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive debugging utilities are used in MOM systems, then users can detect failures after they occur, but the system lacks proactive failure prediction capability and requires deep product knowledge for operation
Solution Approach 1:
The patent implements preliminary action by using machine learning models to predict MOM server failures before they occur. The system analyzes historical server metrics and performance data to identify patterns indicating upcoming failures, allowing the system to take preventive measures such as routing messages to alternate servers before the actual failure happens, thereby transforming reactive debugging into proactive failure prediction
Solution Approach 2:
The patent applies self-service by enabling the MOM system to automatically monitor its own health metrics, predict failures using embedded machine learning models, and autonomously route messages around predicted failures without requiring user intervention or deep product knowledge. The system serves itself by implementing intelligent failure prediction and automatic message routing
2Adaptability or versatility
If heterogeneous MOM vendor libraries are used to integrate different applications, then various MOM platforms can be connected, but the complexity of interfacing increases due to inconsistency across platforms
Solution Approach 1:
The patent implements universality by creating a messaging abstraction layer that provides a unified interface for interacting with multiple heterogeneous MOM platforms. This abstraction layer translates diverse MOM vendor-specific protocols and libraries into a common messaging interface, allowing the system to work with different MOM platforms without requiring separate integration code for each, thereby reducing interfacing complexity while maintaining broad platform compatibility
3Productivity
If messages are routed to the original MOM server without failure prediction, then the system architecture remains simple, but messaging outages and delays occur when server failures happen
Solution Approach 1:
The patent applies the intermediary principle by introducing a messaging abstraction layer that acts as a mediator between applications and MOM servers. This intermediary layer incorporates machine learning-based failure prediction and automatically routes messages to alternate servers when failures are predicted, shielding applications from the complexity of failure management while ensuring reliable message delivery. The abstraction layer handles the complexity of monitoring, prediction, and routing, allowing simple application code to achieve high productivity
Data Source
AI summary
In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a message for delivery to a message-oriented middleware (MOM) server and determining whether an anomaly is predicted in the MOM server. The method also includes, by the computing device, responsive to a determination that an anomaly is predicted in the MOM server, identifying an alternate MOM server for delivery of the message, and routing the message to the alternate MOM server. The method may also include, by the computing device, responsive to a determination that an anomaly is not predicted in the MOM server, delivering the message to the MOM server.