18419062. Message Queue Routing System simplified abstract (Bank of America Corporation)
Contents
- 1 Message Queue Routing System
Message Queue Routing System
Organization Name
Inventor(s)
Anurajam Rajagopalan of Medavakkam IN (US)
Sathyanarayana Rajendran of Chennai IN (US)
Sunil Kumar Sriperambudur of Peerancheru (IN)
Message Queue Routing System - A simplified explanation of the abstract
This abstract first appeared for US patent application 18419062 titled 'Message Queue Routing System
Simplified Explanation
A message queue routing system is implemented into a message queue gateway to route data to a software component deployed in the MQ Gateway, which then routes the data to the MQ cluster. A CPU node analyzer reads real-time health statistics of each processing node in the MQ cluster and routes the message to a node with the minimum CPU load at that time. The CPU node analyzer analyzes CPU performance and system idleness information. Each processing node utilizes an artificial intelligence/machine learning framework and trained predictive models for dynamic message routing computations. The predictive model is trained using text classification on a trained dataset, extracting message information via natural language processing to identify characteristic information and select filters for routing the message to a target application.
- Message queue routing system implemented in a message queue gateway
- CPU node analyzer reads real-time health statistics of processing nodes in the MQ cluster
- Routes message to node with minimum CPU load
- Utilizes artificial intelligence/machine learning framework and trained predictive models for dynamic message routing computations
- Trained predictive model using text classification on a trained dataset
- Extracts message information via natural language processing to select filters for routing the message
Potential Applications
The technology can be applied in various industries such as telecommunications, finance, and healthcare for efficient message routing and load balancing in large-scale systems.
Problems Solved
1. Efficient routing of messages in a message queue system 2. Load balancing among processing nodes in a cluster
Benefits
1. Improved system performance and response time 2. Optimized resource utilization 3. Enhanced scalability and reliability of the system
Potential Commercial Applications
Optimized message routing technology for large-scale systems
Possible Prior Art
Prior art may include existing message queue routing systems and load balancing algorithms used in distributed systems.
Unanswered Questions
== How does the system handle message prioritization in case of high traffic? The system may prioritize messages based on predefined rules or algorithms to ensure critical messages are processed promptly.
== What security measures are in place to protect the message data during routing? The system may implement encryption protocols and access control mechanisms to secure message data during routing.
Original Abstract Submitted
A message queue routing system may be implemented into a message queue gateway to route data to Software component designed and deployed into MQ Gateway to route the data to the MQ cluster. A central processing unit (CPU) node analyzer reads real time health statistics of each of a plurality of MQ cluster processing nodes. Based the computation, the CPU node analyzer routes the message to a specified node of the cluster having a minimum of CPU load at that time. The CPU node analyzer analyzes information comprising at least CPU performance information and system idleness information. Each processing node enables dynamic message routing computations utilizing an artificial intelligence/machine learning framework and a plurality of trained predictive models. The predictive model is trained using a trained data set using text classification. Message information is extracted from the message via natural language processing and is processed via the trained model to identify characteristic information of the message. The characteristic information is used to select a subset of filters associated with message functionality. The message is routed to a target application based on filtering performed using the subset of filters.
