REINFORCEMENT LEARNING-BASED VIRTUAL MACHINE ORCHESTRATION FOR HYBRID OPENSTACK–VMWARE CLOUD ENVIRONMENTS

Abstract

Hybrid cloud infrastructures combine private and enterprise virtualization platforms to provide flexible, scalable computing resources. However, orchestrating virtual machines across heterogeneous environments remains a complex challenge due to differences in resource management frameworks, scheduling policies, and performance constraints. Platforms such as OpenStack and VMware vSphere often operate independently, making cross-platform orchestration inefficient when relying on static scheduling strategies.

This research proposes a reinforcement learning–based orchestration framework that dynamically manages virtual machine placement, migration, and scaling across hybrid cloud infrastructures. The proposed system utilizes a reinforcement learning agent that continuously observes infrastructure states such as CPU utilization, memory consumption, network latency, and workload demand. Based on these states, the agent selects optimal actions for deploying or migrating virtual machines between OpenStack and VMware clusters.

The model learns orchestration policies through interaction with the environment and improves performance over time using reward-based optimization. Experimental evaluations demonstrate improvements in resource utilization efficiency, reduced workload latency, and minimized migration overhead compared to traditional scheduling approaches.

Results indicate that intelligent orchestration significantly enhances operational efficiency in hybrid cloud environments. The proposed framework contributes toward automated infrastructure management by enabling adaptive decision-making within heterogeneous cloud ecosystems.