A Hybrid Deep Reinforcement Learning Framework for Efficient Cloud Resource Scheduling

Abstract

Abstract Cloud computing enables on-demand access to scalable virtualized resources. However, efficient task scheduling in cloud computing remains a challenge because of the dynamic and heterogeneous nature of workloads. This paper proposes a hybrid Deep Reinforcement Learning (DRL) framework that combines Deep Q-Network (DQN), Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) to enable adaptive resource scheduling in cloud environments. The proposed model is implemented using PyTorch and evaluated in a CloudSim based simulation environment Experimental results show that the proposed approach achieves a consistent improvement in terms of makespan reduction and VM utilization compared to individual DRL approaches and classical scheduling algorithms. Experiments were reiterated with multiple runs to ensure reliability and statistical measures are reported. Under the evaluated conditions, the proposed approach shows more efficient scheduling performance, but it has higher computational overhead and is only validated in a simulated environment for now. These results suggest that hybrid DRL-based scheduling is a promising approach for adaptive cloud resource management, with potential for further validation in real-world deployments and energy-aware scenarios.