Typical Continuous Optimization of Kubernetes resources
How DevOps engineers benefit from Continuous Optimization:
Maximize performance and minimize costs in every delivery
Incremental Optimization for quick optimization cycles
Completely automated container sizing optimization
Optimize resources and knobs within containers for even greater speedups
Significantly minimize infrastructure costs
How Continuous Optimization Works:
Using machine-learning technology, Optimizer Studio works to discover the best performing container resources for your specific Kubernetes deployment. Discovering the optimal CPU, memory limits and replicas can now be offloaded to a machine learning model.
By employing advanced parameter search algorithms, incremental optimization, and other features, Optimizer Studio integrates within your CD pipeline to provide Continuous Optimization capability. This means each deployment always gets the most optimal resources, and as a result, the best possible performance at the lowest cost. Optimizer Studio can also optimize within the containers, such as JVM parameters, OS tunables, database settings and web server configuration files for a further performance and efficiency boost.
Typical integration effort for DevOps teams to implement Continuous Optimization of Kubernetes resources into their CD pipeline is one day.