Dynamic Service Migration in Multi-Cloud Architectures Using Proximal Policy Optimization and Reinforcement Learning

Abstract

The rapid proliferation of multi-cloud architectures requires efficient dynamic service migration to mitigate the growing complexity of workload distribution, latency, and resource optimization. Traditional approaches often fail to provide robust, scalable solutions because of their limited adaptability, high latency, and inability to balance competing objectives such as cost, downtime, and SLA compliance. These limitations make the need for an intelligent, automated system capable of optimizing in real time the migration of decisions across heterogeneous cloud environments. This paper proposes the advanced framework based on using Reinforcement Learning (RL) for dynamic service migration in multi-cloud setups. Key methodologies used include PPO for stable global migration decisions, MADDPG for collaborative resource allocation, and HRL to break down complex tasks into high- and low-level optimizations. Model adaptability and training efficiency are enhanced using Transfer Learning for fine-tuning pre-trained models using real-world data, whereas Federated Learning ensures that model updates occur securely and collaboratively across clouds. Reward shaping further accelerates convergence by integrating weighted metrics such as latency, migration cost, and resource utilization. The proposed system achieves notable improvements: >99.95% uptime, ≤1.5 seconds migration downtime, ~90%-95% resource utilization, and ~20%-30% cost reduction. Decision latency is reduced to ≤100ms, and training time is cut by ~50%. This work sets a new benchmark for dynamic service migration in multi-cloud architectures with significant implications for enhancing the reliability and performance of global cloud infrastructure, establishing a scalable, privacy-compliant, and efficient solution for this process.