The rapid growth of Internet of Things (IoT) devices has significantly increased the demand for efficient and scalable cloud resource management. This research presents a hybrid approach combining reinforcement learning (RL) techniques and structured cloud ERP migration strategies to optimize resource allocation in IoT-enabled cloud environments. The study explores Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms for dynamic resource allocation while integrating enterprise system migration considerations. Experimental results demonstrate that PPO outperforms DQN in terms of Service Level Agreement (SLA) compliance, energy efficiency, and resource utilization. Additionally, the study highlights the importance of structured ERP migration strategies in achieving cost efficiency and scalability. The proposed hybrid framework provides a robust solution for modern cloud systems, ensuring adaptive, efficient, and scalable resource management.
Publication Date: 2026-06-05