AI-Driven Autoscaling and Load Balancing for Enterprise Cloud Infrastructure

Abstract

AI-driven autoscaling and load balancing have emerged as critical capabilities for managing the dynamic and complex workloads of enterprise cloud infrastructure. By leveraging machine learning and intelligent analytics, these systems can predict workload patterns, optimize resource allocation, and automatically scale computing resources in real time. AI-based approaches enhance traditional rule-based mechanisms by enabling proactive decision-making, reducing latency, improving application performance, and minimizing operational costs. Furthermore, intelligent load balancing ensures high availability and fault tolerance by distributing workloads efficiently across distributed cloud resources. This integration of AI into cloud infrastructure management supports scalability, resilience, and service-level agreement (SLA) compliance, making it a strategic enabler for modern enterprise IT environments.