Dynamic Resource Allocation Based on Deep Reinforcement Learning Techniques in Multi Cloud Environments

Multi cloud environments have emerged from the growing demand for cloud computing services, which call for effective resource allocation strategies to maximize performance, lower costs, and guarantee dependability of services. Conventional methods of resource allocation sometimes find it difficult to handle changing dynamic workloads and heterogeneous cloud architectures. This work offers a Deep Reinforcement Learning (DRL)-based Dynamic Resource Allocation (DRA) framework for multi-cloud environment optimization of resource provisioning. The proposed method maximizes system efficiency, lowers latency and resource waste, and enables intelligent workload distribution by using Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN). By considering many elements including workload demand, network conditions, and cloud pricing models, the DRL-based approach dynamically learns optimal allocation policies. Experimental results show that in terms of response time, scalability, and cost-effectiveness our method beats traditional heuristic-based and rule-based resource allocation approaches. This work advances autonomous cloud resource management by including deep learning-driven adaptation into multi-cloud ecosystems.