Energy-Efficient Load Balancing in Distributed Insurance Systems Using AI-Optimized Switching Techniques
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P108Keywords:
AI-Optimised Switching, Load Balancing, Distributed Insurance Systems, Reinforcement Learning, Smart Switching Controller, SLA ComplianceAbstract
The growing dependency of the insurance industry on the digital platform in the last decade has dictated the need for a large, robust, and power-efficient IT infrastructure. Distributed insurance systems should be able to support dynamic loads, particularly during periods of peak activity, such as a claims rush or policy renewals, and provide seamless service delivery. Static and rule-based traditional load balancing methods are inadequate in dealing with the increasing complexity, scalability, and energy requirements of contemporary distributed systems. In this paper, we design an AI-optimized switching framework that enhances load balance and reduces energy consumption in distributed insurance systems. The suggested strategy allows combining machine learning techniques comprising reinforcement learning, Long Short-Term Memory (LSTM) networks, and clustering algorithms to predict the demand, optimize the switching processes, and minimize power consumption. A smart switching controller supports dynamic workload reassignment using both predictive and real-time feedback on energy and performance monitors. Simulations of experiments reveal significant reductions in processing delays by up to 40 percent, energy used by up to 30 percent, and system latency as much as 36 percent over existing approaches. Also, resource utilization and throughput figures demonstrate that the balancing based on AI provides a high level of reliability and stability of action under the changing load. The study brings a measurable and environmentally friendly solution to energy-conscious infrastructure management in the insurance industry, leading to intelligent and eco-friendly digitalization. It can serve as a reference point for incorporating AI and sustainability into mission finance services
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