MITB Banner

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.

Share

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.

Share
Picture of Victor Dey

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.
Related Posts

CORPORATE TRAINING PROGRAMS ON GENERATIVE AI

Generative AI Skilling for Enterprises

Our customized corporate training program on Generative AI provides a unique opportunity to empower, retain, and advance your talent.

Upcoming Large format Conference

May 30 and 31, 2024 | 📍 Bangalore, India

Download the easiest way to
stay informed

Subscribe to The Belamy: Our Weekly Newsletter

Biggest AI stories, delivered to your inbox every week.

AI Courses & Careers

Become a Certified Generative AI Engineer

AI Forum for India

Our Discord Community for AI Ecosystem, In collaboration with NVIDIA. 

Flagship Events

Rising 2024 | DE&I in Tech Summit

April 4 and 5, 2024 | 📍 Hilton Convention Center, Manyata Tech Park, Bangalore

MachineCon GCC Summit 2024

June 28 2024 | 📍Bangalore, India

MachineCon USA 2024

26 July 2024 | 583 Park Avenue, New York

Cypher India 2024

September 25-27, 2024 | 📍Bangalore, India

Cypher USA 2024

Nov 21-22 2024 | 📍Santa Clara Convention Center, California, USA

Data Engineering Summit 2024

May 30 and 31, 2024 | 📍 Bangalore, India

Subscribe to Our Newsletter

The Belamy, our weekly Newsletter is a rage. Just enter your email below.