Optimizing Hot Standby Redundancy Using AI for Network Traffic Balancing and Failover Management
DOI:
https://doi.org/10.60087/jklst.v4.n3.002Keywords:
Hot standby redundanc, Artificial intelligence, Network traffic balancing, Failover management, Predictive analytics, High-availability networks, Adaptive algorithms, Network reliabilityAbstract
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.
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