Managing Kubernetes resources is complex and tedious, especially when configuring workloads at massive scale. Built-in Kubernetes capabilities for automated resource management and scalability can’t appropriately analyze resource behavior, which yields suboptimal performance and scalability. The complexity of Kubernetes environments also usually results in poor cost-efficiency – from over-provisioned and under-utilized resources.
Effective management of large-scale containerized apps requires that we optimize the underlying infrastructure – including countless tunable variables. Advanced machine learning helps optimize automation using both observation- and experimentation-based data. This transforms data into actionable intelligence for superior results at scale in both production and non-production Kubernetes environments. We can also optimize the trade-offs between performance, scale and the cost of cloud resources.
Read this White Paper to understand…
- The core aspects of Kubernetes resource management
- How Kubernetes resources impact the pods and containers to which they’re allocated
- Best practices for configuring pod and container resources and the implications for management and scaling at the cluster level
- The roles of machine learning-based analysis and automated configuration tuning
- The capabilities of StormForge: an intelligent scaling and optimization solution that automates resource management and optimizes Kubernetes workloads and applications, leading to a reduction in operating expenses
Take a look inside...
