KubeSphere Virtualization
KubeSphere Virtualization (KSV) is a lightweight VM management platform derived from KubeSphere. KSV supports single-node deployment and multi-node deployment and is easy to use, meeting enterprise-grade virtualization requirements.
Install KSVArchitecture
Uses a cloud-native and loosely coupled architecture that separates the frontend and backend; supports single-node deployment that requires only 4 CPU cores and 8 GB memory as well as multi-node deployment; independent of the infrastructure and compatible with any x86 or Arm machines that support virtualization.
Features
As an enterprise-grade virtualization platform, KSV provides an easy-to-use web console, helping users quickly achieve enterprise hardware resource virtualization.
Node Management
Quickly obtain summary information about cluster nodes and status of each node.
IP Pool Management
Create and delete IP pools for VM network and IP address management.
VM Management
Create and delete VMs, use VM snapshots, and remotely log in to VMs.
Image Management
Manage system images used for VM creation.
Disk Management
Manage VM disks, use disk snapshots, and expand disk capacity.
Project Management
Separate resources into projects and control user permissions based on user roles.
Benefits
Lightweight, flexible, and easy to deliver – based the Kubernetes cloud-native framework, KSV can be easily integrated with various external software and hardware systems into enterprise-grade solutions.
Lightweight Development
Individual users can set up virtualization environments on PCs for new technology learning, application development, software testing, and bug simulation.
Cross-VM Development
Use multiple VMs to simulate a network environment for testing; use VMs with different OSs to perform cross-platform development to fully utilize physical resources, improve efficiency, reduce costs, and simplify management.
Edge Computing and Big Data AI
Deploy KSV on a single node or multiple nodes based on the x86 or Arm architecture; integrate KSV with IoT devices and use AI algorithms and applications to achieve local data processing and ultra-low delay; use GPU passthrough and virtualization to quickly deploy and manage AI/ML applications.