One of the core features of our application and devops container management solution, the Banzai Cloud Pipeline platform, is multi-dimensional autoscaling based on default and custom metrics. Most such solutions gather Prometheus metrics and base Horizontal Pod Autoscaling only on basic CPU & memory metrics. However, scaling on these metrics is often not flexible enough to adapt to the increased load and traffic. window.HELP_IMPROVE_VIDEOJS = false; Therefore, Banzai Cloud Pipeline allows you to scale based on:

One of the main advantages of the Pipeline platform is that it allows users to use their infrastructure cost effectively; Telescopes helps with cluster and machine instance recommendations, Hollowtrees enables SLA-aware cost reduction using spot instances, and autoscalers allow for multi-dimensional autoscaling based on custom metrics. This post will highlight some new features of the Banzai Cloud Horizontal Pod Autoscaler Kubernetes Operator and the advanced automation supported by Pipeline - a new, forward-thinking way to operate Kubernetes clusters and autoscale deployments.

A few months ago the Kubernetes Operator SDK was released with one of its goals being the conversion of human operational knowledge into code. At Banzai Cloud we’ve been contributors and early adopters of this technology, since it provides a better standardized method of automating our processes and allows us to dramatically ease the lives of our customers. We are building a feature rich enterprise-grade application platform, built for containers on top of Kubernetes, called Pipeline, wherein we endeavour to automate the DevOps experience and the lifecycle of deployments.

If you followed our blog series on Autoscaling on Kubernetes, you should already be familiar with Kubernetes’ Cluster autoscaler and the Vertical Pod Autoscaler used with Java 10 applications. This post will show you how to use the Horizontal Pod Autoscaler to autoscale your deployments based on custom metrics obtained from Prometheus. As a deployment example we’ve chosen our JEE Petstore example application on Wildfly to show that, beside metrics like cpu and memory, which are provided by default on Kubernetes, using our Wildfly Operator, all Java and Java Enterprise Edition / Wildfly specific metrics are automatically placed at your fingertips, available in Prometheus, allowing you to easily autoscale deployments.

At Banzai Cloud we provision all kinds of applications to Kubernetes and we try to autoscale these clusters with Pipeline and/or properly size application resources as needed. As promised in an earlier blog post, How to correctly size containers for Java 10 applications, we’ll share our findings on the Vertical Pod Autoscaler(VPA) used with Java 10. VPA sets resource requests on pod containers automatically, based on historical usage, thus ensuring that pods are scheduled onto nodes where appropriate resource amounts are available for each pod.

One of our goals at Banzai Cloud is to eliminate the concept of nodes, insofar as that is possible, so that users will only be aware of their applications and respective resource needs (cpu, gpu, memory, network, etc). Launching Telescopes was a first step in that direction - helping end users to select the right instance types for the job, through Telescopes infrastructure recommendations, then turning those recommendations into actual infrastructure with Pipeline.