OpenNebula is an open-source cloud and edge computing platform designed to build and manage enterprise clouds and virtualized data centers. It enables organizations to orchestrate compute, storage, and networking resources across private, public, and edge infrastructures. OpenNebula supports virtual machines, containers, and Kubernetes clusters within a single environment, offering multi-tenancy, automation, and elasticity. Its architecture aims to unify cloud management, prevent vendor lock-in, and reduce operational complexity and costs.

Key Features

Unified Cloud Management

• Centralized control panel for managing private, public, and edge cloud resources.
• Single layer to orchestrate compute, storage, and networking.

Multi-Tenancy and Federation

• Supports multiple users and organizations with resource isolation.
• Enables federated cloud environments across data centers.

Virtualization and Container Support

• Manages VMs (KVM, VMware, LXD/LXC, Firecracker) and Kubernetes clusters.
• Supports containerized and virtualized workflows in shared environments.

Automation and Elasticity

• Automatic provisioning and scaling of applications and infrastructure.
• Self-service catalog for deploying multi-tier and auto-scaling applications.

Integration and Extensibility

• APIs and command-line tools for integration with external systems.
• Marketplace for cloud appliances and applications.

Edge and Hybrid Cloud Capabilities

• Combines on-premises, public cloud, and edge resources.
• Built-in support for edge deployments and 5G edge AI processing.

Security and Vendor Independence

• Confidential computing and secure application sharing.
• Prevents vendor lock-in by supporting diverse infrastructures.

Benefits

Operational Efficiency

• Reduces complexity and resource consumption through unified management.
• Streamlines cloud operations with automation and self-service features.

Flexibility and Scalability

• Adapts to various workloads and deployment models (private, hybrid, edge).
• Scales from small installations to large federated environments.

Cost Reduction

• Lowers operational costs by optimizing resource usage and supporting open-source technologies.
• Avoids proprietary vendor lock-in, enabling infrastructure choice.

AI and Advanced Workload Support

• Facilitates enterprise-grade AI workloads with GPU, DPU, and LLM integrations.
• Supports high-performance Kubernetes and AI/ML deployments.