Kubeturbo leverages Turbonomic's patented analysis engine to provide visibility and control across the entire stack in order to assure the performance of running micro-services in Kubernetes Pods, as well as the efficiency of underlying infrastructure.
- Turbonomic 5.9+ installation
- Kubernetes 1.4+
- Deploy Kubeturbo
- Once deployed, corresponding targets will show up in Turbonomic UI
- Full-Stack Visibility by leveraging 50+ existing Turbonomic controllers, from on-prem DataCenter to major public cloud providers. No more shadow IT
- From Load Balancer all the way down to your physical Infrastructure
- Real-Time resource monitoring across entire DataCenter
- Real-Time Cost visibility for your public cloud deployment
- Provide Rescheduler capability by leveraging The Turbonomic analysis engine (Execution of moving pods requires Kubeturbo to be the scheduler of the pod)
- Consolidating Pods in real-time to increase node efficiency
- Reschedule Pod in advance to prevent suffering resource congestion from the underlying node
- Reschedule Pod to new node added to the cluster
- Reschedule Pods that peak together to different nodes, to avoid performance dropping
- Right-Sizing your Pod and your entire IT stack
- Combining Turbonomic real-time performance monitoring and analysis engine, Turbonomic is able to provide right-sizing information for each individual pod as well as the entire IT stack.
- Right-sizing up your Pod limit, if necessary, to avoid OOM
- Right-sizing down your Pod requested resource, if necessary, to avoid resource overprovisioning or overspending in public cloud deployment.
- Support for Cluster Federation Control Plane
- Complete visibility for your K8s deployments across different underlying infrastructures
- Create affinity/anti-affinity policies directly from Turbonomic UI
- Improve cost efficiency by consolidating workload across deployments and identifying the cheapest region and provider to deploy your workload
- What-If Planner
- A complete What-If sandbox to help you plan your IT changes in advance
- Plan for workload change: Add/Remove Containers
- Plan for infrastructure change: Add/Remove/Replace hardware
- Plan for Cloud Migration: Expense and Savings
- Cluster Consolidation for federated clusters