Self-Managing Kubernetes

Intelligent Workload Automation for Kubernetes

Kubernetes has taken the lead in helping organizations deliver the cloud native applications that drive digitization. It gives developers the means to create better applications and services faster. But for IT, it makes the environment more dynamic and complex.

Key Features & Benefits

Self-managing Kubernetes optimizes performance, efficiency, and compliance so IT organizations can scale and accelerate cloud native intiatives.

Minimal human intervention – no thresholds to set!

Automated rescheduling of pods assures performance

Intelligent cluster scaling ensures elastic infrastructure

Full-stack control unites DevOps and Infrastructure

Decision Automation for Kubernetes

Turbonomic makes workloads smart—enabling them to self-manage—and determines the specific actions that will drive continuous health:

  • Continuous placement for Pods (rescheduling)
  • Continuous scaling for applications and  the underlying cluster.

It assures application performance by giving workloads the resources they need when they need them.

Trustworthy Actions Allow Real-Time Automation

Trustworthy actions account for all compute, network, and storage needs of Pods, Nodes, and Infrastructure. With accurate analysis of the real-time environment, actions can be automated to achieve self-managing Kubernetes clusters.

Continuous Health

Continuous health enables DevOps and IT teams to achieve true agility, freeing resources for new projects and services.

Unified Workload Automation for Hybrid Clouds

Unified workload automation intelligently controls Kubernetes clusters across on-prem and public cloud environments.

“Our collaboration with Turbonomic assures the performance of any workload, whether running on private or public cloud, and helps to accelerate delivery of new applications at scale.”

 – Ian Penny, CTO, Barclays

Visit github.com/turbonomic/kubeturbo to get started.

How Does It Work?

Turbonomic uses a container—KubeTurbo—that runs in your Kubernetes or Red Hat OpenShift cluster to discover and monitor your environment. KubeTurbo runs together with the default scheduler and sends this data back to the Turbonomic analytics engine. Turbonomic determines the right actions that drive continuous health, including continuous placement for Pods and continuous scaling for applications and the underlying cluster.

See what our Workload Automation for Hybrid Cloud can do for you.

Decisions in under an hour. Payback in less than 3 months.

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