K8s App Resource Management: Use Cases Overview
Take back control. Manage a multi-tentant platform, optimize containerized workloads and cluster capacity, and leverage Turbonomic actions to deploy more applications faster, safely with less resources and at the lowest cost all while assuring application performance.
- Overview
- Getting Started
- Full Stack Management
- Optimized Vertical Scaling
- Effective Cluster Management
- Intelligent SLO Scaling
- Proactive Rescheduling
- Better Cost Management
KubeTurbo leverages Turbonomic's patented analysis engine to provide visibility, control and optimization across the entire application stack in order to assure the performance of running micro-services in Kubernetes and Red Hat OpenShift, as well as the efficiency of underlying infrastructure.
- Review the Prerequisites and Deploy Kubeturbo.
- Once deployed, corresponding Kubernetes and Red Hat OpenShift clusters will show up in Turbonomic UI and in Settings > Targets and then you will see the following:
- Child/Parent relationship context from Services to Workloads in the Platform to the underlying Infrastructure
- Actions that automate Application Resource Management
See blog here for updated content
Manage the Trade-offs of Performance and Efficiency with Intelligent Vertical scaling that understands the entire IT stack
- Combining Turbonomic real-time performance monitoring and analysis engine, Turbonomic is able to provide right-sizing and scaling decisions for each service as well as the entire IT stack.
- Right-sizing up your Pod limit to avoid OOM and address CPU Throttling
- Right-sizing down your Pod requested resource to avoid resource over-provisioning or overspending in public cloud deployment.
- Actions based on historical data for every replica past and present
Intelligently scale the capacity of your cluster with better analytics to determine when nodes should suspend or provision. Analysis that is based on usage, requests, not just pod pending conditions! will save time, money and assure app availability.
Manage the Trade-offs of Performance and Efficiency with Intelligent Vertical and Horizontal scaling that understands the entire IT stack
- Leverage application SLO KPIs of Response Time, Transaction Throughput from any source (APM tools like Instana, custom metrics via Prometheus) to drive actions.
- Scale services based on SLO and simultaneously managed cluster resources to mitigate pending pods
Intelligently and continuously redistribute a workload under changing conditions by leveraging the Turbonomic analysis engine
- Consolidate pods in real-time to increase node efficiency
- Reschedule pod to prevent performance degradation due to resource congestion from the underlying node
- Redistribute pods to leverage resources when new node capacity comes on line
- Reschedule pods that peak together to different nodes, to avoid performance issues due to "noisy neighbors"
Learn more by going to this article Turbonomic Pod Moves - continuous rescheduling!
Understand the cost of running the platform, and leverage Turbonomic data to objectively describe how much each tenant is using by namespace.
