Kubernetes Resource Management at Scale

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.