K8GB is a Global Service Load Balancing solution with a focus cloud and Kubernetes nativity. Global load balancing/GSLB (Global Server Load Balancing) solutions have typically been the domain of proprietary network software and hardware vendors, installed and managed by siloed network teams. k8gb is a completely open source, cloud-native, global load balancing solution developed for Kubernetes. k8gb uses multiple strategies to manage load balancing traffic across geographically dispersed Kubernetes clusters to meet requirements such as region failover for high availability.
Global load balancing for any Kubernetes Service can now be enabled and managed by any operations or development teams in the same Kubernetes-native way as any other custom resource.
In this OpenShift Commons Briefing, Red Hat’s Paul Morie talks to Absa Group's Yuri Tsarev. Tsarev introduces K8GB, then takes questions, discusses the development roadmap and more.
Here’s a link to the slides from this talk.
Ultimately, K8GB's goal is to provide a cloud-native GSLB that is lightweight both in terms of resource requirements and runtime complexity, and one that plays well with Kubernetes. It enables end users to define their GSLB configuration natively using Kubernetes, with a high level of availability and redundancy that can run multiple instances with shared state, spanning multiple data centers or clouds.
More information is available at the K8GB web site
K8GB installation and configuration tutorials
K8GB Changelog
We are looking forward to meeting and interacting with our community and hearing your use cases, feedback and suggestions for future enhancements.
Join the OpenShift Commons to hear about future OpenShift Commons Briefings and join the conversation globally to help enable the open source, container-driven hybrid cloud. Make sure to register for OpenShift Commons Gathering Kubecon EU, a virtual experience on May 4th in Central European Standard Time (CEST) focused on talks from end users with production deployments of OpenShift sharing their use cases and lessons learned.
