Topology Explorer for Message-Oriented Middleware Using Machine Learning
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Solution Overview
Problem
Conventional message-oriented middleware management techniques are resource-intensive and reactive, leading to outages and delays due to heterogeneity across platforms and the need for specific system knowledge, making it complex to interface and manage messaging topologies effectively.
Innovation Solution
The implementation of machine learning techniques for message-oriented middleware topology explorers, which predict anomalies and recommend alternate topologies, enabling automated actions to mitigate outages and improve data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional message-oriented middleware management techniques are used, then specific system knowledge and reactive management are required, but this leads to resource-intensive outages and delays
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict anomalies and potential failures in messaging topologies before they occur. The system analyzes historical and real-time data to identify patterns that precede outages, enabling proactive intervention and alternate topology preparation, thus preventing resource-intensive outages and delays
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring messaging topology performance and using this information to train and refine machine learning models. The system feeds back predicted anomalies to operators and automatically adjusts routing decisions based on learned patterns, improving both reliability and productivity through iterative optimization
2Adaptability or versatility
If heterogeneous source and target applications are integrated across message-oriented middleware platforms, then different libraries and user interfaces are required, but this adds complexity to interfacing actions
Solution Approach 1:
The patent applies universality by creating a unified machine learning-based management layer that works across heterogeneous messaging platforms. The system uses platform-agnostic metrics and patterns to predict anomalies and generate recommendations, eliminating the need for platform-specific management approaches and reducing interfacing complexity while maintaining broad compatibility
Solution Approach 2:
The patent introduces an intermediary machine learning system that sits between heterogeneous applications and messaging platforms. This intermediary translates diverse platform-specific behaviors into unified anomaly predictions and recommendations, simplifying the interfacing layer while maintaining adaptability across different platforms, libraries, and user interfaces
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for implementing topology explorers for message-oriented middleware using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one messaging topology associated with at least one message-oriented middleware; predicting one or more anomalies associated with the at least one messaging topology by processing at least a portion of the obtained data using a first set of one or more machine learning techniques; recommending one or more alternate messaging topologies associated with the at least one message-oriented middleware by processing at least a portion of the one or more predicted anomalies and at least a portion of the obtained data using a second set of one or more machine learning techniques; and performing one or more automated actions based on the one or more predicted anomalies and/or the one or more recommended alternate messaging topologies.