Enhancing Messaging Systems with AI: Predictive Load Balancing in JMS and IBM MQ
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Abstract
Messaging systems like Java Message Service (JMS) and IBM MQ are integral to enterprise applications, ensuring reliable communication between distributed systems. However, as these systems scale, managing message queues and optimizing performance through efficient load balancing becomes increasingly complex. This paper introduces an AI-powered predictive load balancing approach designed to enhance the performance of JMS and IBM MQ messaging systems. By leveraging machine learning algorithms, such as time-series forecasting and regression models, the proposed framework predicts incoming message traffic and dynamically adjusts load balancing strategies to optimize message delivery and system resource utilization. The system learns from historical traffic patterns, real-time system metrics, and environmental factors to proactively allocate resources, minimize message latency, and prevent bottlenecks. Experimental results show significant improvements in system throughput, reduced message processing delays, and higher availability compared to traditional load balancing techniques. This AI-driven approach not only enhances the efficiency of messaging systems but also provides a scalable and adaptive solution to ensure consistent performance in highly dynamic environments.
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References
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