Containers

Category: Amazon Elastic Kubernetes Service

Operating resilient workloads on Amazon EKS

Introduction When the margin for error is razor thin, it is best to assume that anything that can go wrong will go wrong. AWS customers are increasingly building resilient workloads that continue to operate while tolerating faults in systems. When customers build mission-critical applications on AWS, they have to make sure that every piece in […]

Optimize webSocket applications scaling with API Gateway on Amazon EKS

Introduction WebSocket is a common communication protocol used in web applications to facilitate real-time bi-directional data exchange between client and server. However, when the server has to maintain a direct connection with the client, it can limit the server’s ability to scale down when there are long-running clients. This scale down can occur when nodes […]

Analyze EKS Fargate costs using Amazon Quicksight

Introduction AWS Fargate is a serverless compute engine for running Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon Elastic Container Service (Amazon ECS) workloads without managing the underlying infrastructure. AWS Fargate makes it easy to provision and scale secure, isolated, and right-sized compute capacity for containerized applications. As a result, teams are increasingly choosing AWS […]

How to upgrade Amazon EKS worker nodes with Karpenter Drift

[May, 2024 – This blog has been updated to reflect Karpenter v1beta1 API changes] Introduction Karpenter is an open-source cluster autoscaler that provisions right-sized nodes in response to unschedulable pods based on aggregated CPU, memory, volume requests, and other Kubernetes scheduling constraints (e.g., affinities and pod topology spread constraints), which simplifies infrastructure management. When using […]

Amazon EKS extended support for Kubernetes versions available in preview

Introduction Today, we’re announcing the preview of Amazon Elastic Kubernetes Service (EKS) extended support for Kubernetes versions. You can now run Amazon EKS clusters on a Kubernetes version for up to 26 months from the time the version is generally available on Amazon EKS. Extended Support is available as a free preview for all Amazon […]

Use shared VPC subnets in Amazon EKS

Introduction In the ever-changing landscape of cloud computing, organizations continue to face the challenge of effectively managing their virtual network environments. To address this challenge, many organizations have embraced shared Amazon virtual private clouds (VPCs) as a means to streamline network administration, and reduce costs. Shared VPCs not only provide these advantages but also enable […]

Amazon EKS now supports Kubernetes version 1.28

Introduction The Amazon Elastic Kubernetes Service (Amazon EKS) team is pleased to announce support for Kubernetes version 1.28 for Amazon EKS and Amazon EKS Distro. Amazon EKS Anywhere (release 0.18.0) also supports Kubernetes 1.28. The theme for this version was chosen as a play on words that combines plant and Kubernetes to evoke the image […]

Recent changes to the CoreDNS add-on

Introduction Amazon Elastic Kubernetes Service (Amazon EKS) add-ons were originally introduced in December 2021. At launch, they provided a mechanism for installing and managing a curated set of add-ons for Amazon EKS clusters. The add-on for CoreDNS was amongst the first add-ons we released because DNS plays such a pivotal role in Kubernetes. When advanced […]

Explore etcd Defragmentation in Amazon EKS

Introduction Amazon Elastic Kubernetes Service (Amazon EKS) has gained significant popularity as a managed Kubernetes service, providing a scalable and reliable platform for running containerized applications. Behind the scenes, Amazon EKS uses etcd, a distributed key-value store, to store cluster configuration, state, and metadata. In this post, we delve into the defragmentation functionality in etcd and discuss the […]

GPU sharing on Amazon EKS with NVIDIA time-slicing and accelerated EC2 instances

In today’s fast-paced technological landscape, the demand for accelerated computing is skyrocketing, particularly in areas like artificial intelligence (AI) and machine learning (ML). One of the primary challenges the enterprises face is the efficient utilization of computational resources, particularly when it comes to GPU acceleration, which is crucial for ML tasks and general AI workloads. […]