Containers

Tag: Amazon CloudWatch

ECS auto scaling using custom metrics

Amazon Elastic Container Service (ECS) Auto Scaling using custom metrics

Introduction Amazon ECS eliminates the need to install, operate, and scale your own cluster management infrastructure. Customers are using horizontal scalability to deploy and scale their microservices applications running on Amazon ECS. They use the Application Auto Scaling service to automatically scale based on metrics data. Amazon ECS typically measures service utilization based on average […]

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Introducing Amazon CloudWatch Container Insights for Amazon EKS Fargate using AWS Distro for OpenTelemetry

Introduction Amazon CloudWatch Container Insights helps customers collect, aggregate, and summarize metrics and logs from containerized applications and microservices. Metrics data is collected as performance log events using the embedded metric format. These performance log events use a structured JSON schema that enables high-cardinality data to be ingested and stored at scale. From this data, […]

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Controlling and monitoring AWS App Runner applications with Amazon EventBridge

Many applications do not need to be available 24/7, such as those in development and QA environments. AWS App Runner supports this and allows applications to be paused, or deactivated, to lower costs when not in use. The applications can then be resumed or activated when they are needed. This blog post uses this example […]

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Welcome IIS webpage

Running Windows Containers with Amazon ECS on AWS Fargate

At AWS, customers are running their most mission-critical workloads on Amazon Elastic Container Service (Amazon ECS) with Windows as their compute layer. Still, the undifferentiated heavy lifting of managing the underlying host OS, patching, scaling, and hardening when running Windows containers are time-consuming tasks. Therefore, customers can choose to use the optimized AMIs, which are preconfigured […]

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Introducing CloudWatch Container Insights Prometheus Support with AWS Distro for OpenTelemetry on Amazon ECS and Amazon EKS

You can use CloudWatch Container Insights to monitor, troubleshoot, and alarm on your containerized applications and microservices. Amazon CloudWatch collects, aggregates, and summarizes compute utilization information like CPU, memory, disk, and network data. It also helps you isolate issues and resolve them quickly by providing diagnostic information like container restart failures. Container Insights gives you […]

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Autoscaling Amazon ECS services based on custom CloudWatch and Prometheus metrics

Introduction Horizontal scalability is a critical aspect of cloud native applications. Microservices deployed to Amazon ECS leverage the Application Auto Scaling service to automatically scale based on observed metrics data. Amazon ECS measures service utilization based on CPU and memory resources consumed by the tasks that belong to a service and publishes CloudWatch metrics, namely, […]

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Gif that shows canary deployment taking place

Create a pipeline with canary deployments for Amazon EKS with AWS App Mesh

In this post, we will demonstrate how customers can leverage different AWS services in conjunction with AWS App Mesh to implement a canary deployment strategy for applications running on Amazon Elastic Kubernetes Service (Amazon EKS). As stated in the post “Getting started with App Mesh and EKS”, many customers are currently implementing microservices in a […]

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Autoscaling Amazon EKS services based on custom Prometheus metrics using CloudWatch Container Insights

Introduction In a Kubernetes cluster, the Horizontal Pod Autoscaler can automatically scale the number of Pods in a Deployment based on observed CPU utilization and memory usage. The autoscaler depends on the Kubernetes metrics server, which collects resource metrics from Kubelets and exposes them in Kubernetes API server through Metrics API. The metrics server has […]

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CI/CD pipeline for testing containers on AWS Fargate with scaling to zero

Development teams are running manual and automated tests several times a day for their feature branches. Running tests locally is only one part of the process. To test workloads against other systems as well as give access to QA engineers, it requires deploying code to dedicated environments. These servers/VMs spend hours idling because new test […]

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