Scalable Load Balancing Strategies for Cloud-Native Data Systems Using Hybrid AI-Driven Decision Models
Keywords:
Cloud-native systems, AI-driven decision models, Load balancing, Reinforcement learning, Kubernetes, Scalability, Edge computingAbstract
As cloud-native architectures continue to dominate enterprise and scientific computing environments, ensuring high availability, efficiency, and fault tolerance through robust load balancing strategies becomes a critical priority. Traditional load balancing approaches, while effective in static or semi-dynamic environments, face significant challenges in handling the dynamic, distributed, and microservices-driven nature of modern cloud-native systems. This paper proposes and evaluates hybrid AI-driven decision models for load balancing that integrate reinforcement learning and heuristic optimization techniques. These models dynamically adapt to workload variations and infrastructure heterogeneity in real time, offering scalable and intelligent load distribution mechanisms. The paper further presents a comparative analysis of traditional versus hybrid AI-driven load balancing strategies, evaluating their scalability, latency, and resource utilization in Kubernetes-based environments.
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