Intelligent Auto-Scaling in Hybrid and Multi-Cloud Architectures Using Reinforcement Learning

Cloud computing has revolutionized IT resource management by offering scalability, flexibility, and cost-effectiveness. Auto-scaling, the dynamic adjustment of resources to match workload demands, is crucial. Reinforcement Learning (RL) offers a promising alternative by learning optimal resource allocation policies through environmental interaction. This review examines RL-based predictive auto-scaling, discussing key concepts, optimization goals, and workload characteristics. Emerging trends like deep learning integration, meta-reinforcement learning, green computing, and multi-objective RL demonstrate RL’s potential for advancing auto-scaling. Future directions include hybrid approaches, continuous learning frameworks, and energy-efficient solutions.