AWS Releases Open Source High-Performance Kubernetes Cluster Autoscaler

When Karpenter is installed in a working cluster, it observes the aggregate resource requests of unscheduled pods and makes decisions to launch new nodes and terminate them to reduce scheduling latencies and infrastructure costs.

AWS recently announced the release of Karpenter, an open-source, flexible, high-performance Kubernetes cluster autoscaler built with AWS. It helps improve application availability and cluster efficiency by rapidly launching right-sized compute resources in response to changing application load. Karpenter also provides just-in-time compute resources to meet applications’ needs and will soon automatically optimize a cluster’s compute resource footprint to reduce costs and improve performance.

Before Karpenter, Kubernetes users needed to dynamically adjust the compute capacity of their clusters to support applications using Amazon EC2 Auto Scaling groups and the Kubernetes Cluster Autoscaler. Nearly half of Kubernetes customers on AWS report that configuring cluster autoscaling using the Kubernetes Cluster Autoscaler is challenging and restrictive.

When Karpenter is installed in a working cluster, it observes the aggregate resource requests of unscheduled pods and makes decisions to launch new nodes and terminate them to reduce scheduling latencies and infrastructure costs. Karpenter does this by observing events within the Kubernetes cluster and then sending commands to the underlying cloud provider’s compute service, such as Amazon EC2.

Karpenter is an open-source project licensed under the Apache License 2.0. It is designed to work with any Kubernetes cluster running in any environment, including all major cloud providers and on-premises environments. Kapenter provisioners are designed to work alongside static capacity management solutions like Amazon EKS managed node groups and EC2 Auto Scaling groups. You may choose to manage the entirety of your capacity using provisioners, a mixed model with both dynamic and statically managed capacity, or a fully static approach.

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Victor Dey
Victor is an aspiring Data Scientist & is a Master of Science in Data Science & Big Data Analytics. He is a Researcher, a Data Science Influencer and also an Ex-University Football Player. A keen learner of new developments in Data Science and Artificial Intelligence, he is committed to growing the Data Science community.

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