1 year ago

With the rise of containerized services based on service-oriented architecture (SOA), the need for orchestration software like Kubernetes is rapidly increasing. The Kubernetes architecture makes autonomous workload allocation decisions within a cluster.

But, as the following sections show, manually optimizing these parameters is time-consuming and ineffective due to Kubernetes’ complexity.

Besides the Kubernetes technical components, such as CPU or memory, we should also look at the application-specific parameters.

With the power of ML, automation can alleviate the complexities of configuring multiple Kubernetes parameters, optimizing the trade-off between performance and cost.