Optimizing Hot Standby Redundancy Using AI for Network Traffic Balancing and Failover Management
Year: 2025
Type: article
Abstract: This research explores the application of artificial intelligence (AI) techniques to optimize hot standby redundancy mechanisms in network systems, focusing on enhancing traffic balancing and failover management. By leveraging AI-driven predictive analytics and adaptive algorithms, the proposed approach dynamically distributes network traffic across primary and standby nodes to minimize latency and maximize resource utilization. The system proactively detects potential failures and orchestrates seamless failover processes, thereby improving network reliability and reducing downtime. Experimental results demonstrate significant improvements in traffic throughput, failover response time, and overall system resilience compared to traditional redundancy models. This study provides a robust framework for implementing intelligent redundancy solutions in modern high-availability networks.
Authors: Mohan Vamsi Musunuru, Chiranjeevi Devi, Swaminathan Sethuraman
Institutions: Amazon (United States), Visa (United States)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413417286