AI-Driven Optimization of Backend Systems: Enhancing Performance and Reducing Latency in Large-Scale Applications
Abstract
As large-scale applications continue to grow in complexity and user demand, ensuring backend performance and minimizing latency have become critical priorities for system architects and developers. This paper explores the application of Artificial Intelligence (AI) techniques to optimize backend systems, focusing on intelligent resource allocation, dynamic load balancing, predictive caching, and anomaly detection. Leveraging machine learning models, we propose a framework that continuously analyzes system telemetry to make real-time decisions, thereby reducing response time and enhancing throughput. Experimental evaluations demonstrate significant performance gains across diverse backend architectures, with up to 40% reduction in latency and improved fault tolerance. This research highlights the transformative potential of AI in backend infrastructure and provides insights into practical deployment strategies for scalable, self-optimizing systems.
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