I am using AWS cloud ECS services and created once ECS cluster. In which there is one FargateService and behind that 2 containers are running.
Two alarms have been created for fargate service on threashold 95.
Everything looks fine now Testing part comes into picture. I want test alarms functionality.
is there any easy way in AWS using some AWS service or manual script so that I can increase CPU and Memory use to test alarm functionality .
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For my microservice based application, I am designing a component which is as follows:
Task that we want to execute is of periodic nature. For it, i planned to make use of the Kubernetes cron-jobs. It executes the job every 1 hour. This works perfectly fine.
In few scenarios, i want to execute this task on-demand (in stead of waiting for next hour window). For example, if next job time is 2:00pm, i want to execute it early, say 1:20pm.
There is a related question - How can I trigger a Kubernetes Scheduled Job manually?
But I am not looking for a manual way of achieving it or explicitly calling kubectl
commands. Is there a way do it automatically, based on events/queues?
Our application is deployed on AWS EKS and Azure AKS. Can I integrate the k8 clusters to read onto some queues/pub-subs (ex. aws-sqs, aws-sns) and do it dynamically?
Your help would be immensely appreciated!
If you application is running on Kubernetes and don't want to get migrated to serverless function and keep everything inside the Kubernetes cluster you can use the Knative.
Scale to Zero With Knative
Knative is a serverless platform that is built on top of Kubernetes. It provides higher-level abstractions for common application use cases.
One key feature is its ability to run generic (micro) service-based applications as serverless with the help of built-in scale to zero support. Knative has introduced its own autoscaler, Knative Pod Autoscaler (KPA), that supports scale to zero for any service that uses non-CPU-based scaling matrics.
update your micro service to running with Knative minor change will be there and you can run it on Kubernetes.
I'm trying to create an alarm by using memory utilization in AWS Batch. However, the metric related to this service is under the ECS Cluster that is automatically created when creating a compute environment. I'm trying to provide this cluster name to the alarm dimension, but I'm unable to access the cluster name using CDK. I've researched in the CDK API and it doesn't seem to be possible. Does anybody now how this can be done?
I don't know whether you can find the ECS Cluster created by Batch using CDK. Batch hides the details about the work that it does on the backend (i.e. creating an ECS Cluster).
My only guess is that you can write custom code to list the ECS Clusters in your account and match one of the clusters with the name you expect to see. I think Batch initializes the cluster when you initialize the Batch Compute Environment, but I'm not sure whether there is a lag in the timing.
I need to deploy a GPU intensive task on GCP. I want to use a Node.js Docker image and within that container to run a Node.js server that listens to HTTP requests and runs a Python image processing script on-demand (every time that a new HTTP request is received containing the images to be processed). My understanding is that I need to deploy a load balancer in front of the K8s cluster that has a static public IP address which then builds/launches containers every time a new HTTP request comes in? And then destroy the container once processing is completed. Is container re-use not a concern? I never worked with K8s before and I want to understand how it works and after reading the GKE documentation this is how I imagine the architecture. What am I missing here?
runs a Python image processing script on-demand (every time that a new HTTP request is received containing the images to be processed)
This can be solved on Kubernetes, but it is not a very common kind of workload.
The project that support your problem best is Knative with its per-request auto-scaler. Google Cloud Run is the easiest way to use this. But if you want to run this within your own GKE cluster, you can enable it.
That said, you can also design your Node.js service to integrate with the Kubernetes API-server to create Jobs - but it is not a good design to have common workload talk to the API-server. It is better to use Knative or Google Cloud Run.
I was thinking of testing out Google's Cloud Run for a simple app when all of a sudden I got thinking as to whether Cloud Run is basically a managed K8s cluster. I really wanted to know as to when using Cloud Run would be preferred over traditional K8s clusters and why we should prefer it?
Thanks.
Technology wise, cloud Run is a managed Kubernetes cluster with Knative to run the containers on top of it.
However Cloud Run brings an additional advantages when you run fully managed: you only pay for used resources. In other words, Cloud Run can do scale down to zero cost, rather than bottoming out at the cost of keeping a minimum sized cluster running.
I use Amazon ECS for my services. ECS autoregisters the containers with the ELB. That is all fine.
However if one of my application gets into a failure mode I want to take it out of the ELB but keep it running so I can do forensic on it.
If I remove it from the ELB manually then ECS will just add it back in.
The AutoScaling group has a "standby" state. I guess it is something similar I am looking for in ECS.
Is that possible somehow?