Releases: alibaba/hybridnet
Releases · alibaba/hybridnet
v0.3.2
v0.3.1
v0.3.0
New features
- Support multicluster feature, which can connect the network between the two clusters (pod ip only)
Improvements
- Recycle IP instances for Completed or Evicted pods
- Use controller-gen to generate crd init yaml file
Fixed Issues
- Fix masquerade error sometimes overlay pod access to underlay pod
- Fix high CPU cost of hybridnet daemon in large scale cluster
- Fix wrong underlay pod scheduling if not all the nodes belong to an underlay network while an overlay network exists
v0.2.1
v0.2.0
New features
- Change project name to "hybridnet", which is completely forward-compatible
Improvements
- Network type will be auto selected while pod has a specified network
Fixed Issues
- Fix wrong masquerading for remote pod to access local pod (update daemon image and rebuild pod will take effect)
v0.1.2
v0.1.1
Improvements
- Add checks for pod using the same subnet with node
- Support setting linux kernel neigh gc thresh parameters
- Only choose vtep and node ip as node internal overlay container networking ip, support extra selection
- Remove duplicated routes
- Adapt to underlay physical environment with arp sender ip check
- Add prechecking for check pod network configuration, if not ready, pod will not be created successfully
Fixed Issues
- Fix error data path for overlay pod to access underlay gateway and excluded ip addresses
v0.1.0
New features
- Support overlay (vxlan) network
- Support hybrid overlay/underlay container network
- Full support for ipv4/ipv6 dual-stack
Improvements
- Node need only one physical nic if container network is in the same vlan with node network
- Non-zero-netId subnet and zero-netId subnet can be on the same node
- Webhook configuration can be managed by an independent yaml
- Use default-ip-retain global flag and ip-retain pod annotation to reallocate/retain IP
Fixed Issues
- Remove overlay logs for underlay-only mode
- Fix error of using prefer interfaces list
- Fix timeout error of pod creation on large scale