I have deployed an app using Kubernetes to a Google Cloud Container Engine Cluster.
I got into autoscaling, and I found the following options:
Kubernetes Horizontal Pod Autoscaling (HPA)
As explained here, Kubernetes offers the HPA on deployments. As per the docs:
Horizontal Pod Autoscaling automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization
Google Cloud Container Cluster
Now I have a Google Cloud Container Cluster using 3 instances, with autoscaling enabled. As per the docs:
Cluster Autoscaler enables users to automatically resize clusters so that all scheduled pods have a place to run.
This means I have two places to define my autoscaling. Hence my questions:
Is a Pod the same as VM instance inside my cluster, or can multiple Pod's run inside a single VM instance?
Are these two parameters doing the same (aka creating/removing VM instances inside my cluster). If not, what is their behaviour compared to one another?
What happens if e.g. I have a number of pods between 3 and 10 and a cluster with number of instances between 1 and 3 and autoscaling kicks in. When and how would both scale?
Many thanks!
Is a Pod the same as VM instance inside my cluster, or can multiple
Pod's run inside a single VM instance?
Multiple Pods can run the same instance (called node in kuberenetes). You can define maximum resources to consume for a POD in the deployment yaml. See the docs. This is an important prerequisite for autoscaling.
Are these two parameters doing the same (aka creating/removing VM
instances inside my cluster). If not, what is their behaviour compared
to one another?
Kubernetes autoscaler will schedule additional PODs in your existing nodes. Google autoscaler will add worker nodes (new instances) to your cluster. Google autoscaler looks at queued up PODs that cannot be scheduled because there is no space in your cluster and when it finds those will add nodes.
What happens if e.g. I have a number of pods between 3 and 10 and a
cluster with number of instances between 1 and 3 and autoscaling kicks
in. When and how would both scale?
By the maximum resource usage you define for your pods google autoscaler will estimate how many new nodes are required to run all queued up, scheduled pods.
Also read this article.
Related
I have a project on Kubernetes and only 1 pod is running on each node and it has to be like that. How can I autoscale the nodes I want to build? So how can I create a new node when the load on the node increases. I am using AWS and Azure.
I am not sure why you want to run a pod per node. We have separate groups of applications and each group runs on certain node groups. We use cluster autoscaler to scale down/up nodes based on usage. We then use taints and tolerations on pods/nodes for each group of application. Cluster auto scaler will scale up nodes for the specific application group is pods are in pending status.
added below portion -
One useful article about scaling in kubernetes is here. You can read about Cluster Autoscaler (CA) as well.
I think you can try this with ClusterAutoscaler:
https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler
I have a tiny Kubernetes cluster consisting of just two nodes running on t3a.micro AWS EC2 instances (to save money).
I have a small web app that I am trying to run in this cluster. I have a single Deployment for this app. This deployment has spec.replicas set to 4.
When I run this Deployment, I noticed that Kubernetes scheduled 3 of its pods in one node and 1 pod in the other node.
Is it possible to force Kubernetes to schedule at most 2 pods of this Deployment per node? Having 3 instances in the same pod puts me dangerously close to running out of memory in these tiny EC2 instances.
Thanks!
The correct solution for this would be to set memory requests and limits correctly matching your steady state and burst RAM consumption levels on every pod, then the scheduler will do all this math for you.
But for the future and for others, there is a new feature which kind of allows this https://kubernetes.io/blog/2020/05/introducing-podtopologyspread/. It's not an exact match, you can't put a global cap, rather you can require pods be evenly spaced over the cluster subject to maximum skew caps.
I am using kubernete to manage docker cluster. Right now, I can set up POD autoscale using Horizontal Pod Scaler, that is fine.
And now I think the next step is to autoscale nodes. I think for HPA, the auto-created pod is only started in the already created nodes, but if all the available nodes are utilized and no available resource for any more pods, I think the next step is to automatically create node and have node join the k8s master.
I googled a lot and there are very limited resources to introduce this topic.
Can anyone please point me to any resource how to implement this requirement.
Thanks
One way to do using AWS and setting up your own Kubernetes cluster is by following these steps :
Create an Instance greater than t2.micro (will be master node).
Initialize the Kubernetes cluster using some tools like Kubeadm. After the initialisation would be completed you would get a join command, which needs to e run on all the nodes who want to join the cluster. (Here is the link)
Now create an Autoscaling Group on AWS with start/boot script containing that join command.
Now whenever the utilisation specified by you in autoscaling group is breached the scaling would happen and the node(s) would automatically join the Kubernetes cluster. This would allow the Kubernetes to schedule pods on the newly joined nodes based on the HPA.
(I would suggest to use Flannel as pod network as it automatically removes the node from Kubernetes cluster when it is not available)
kubernetes operations (kops) helps you create, destroy, upgrade and maintain production-grade, highly available, Kubernetes clusters from the command line.
Features:
Automates the provisioning of Kubernetes clusters in AWS and GCE
Deploys Highly Available (HA) Kubernetes Masters
Most of the managed kubernetes service providers provide auto scaling feature of the nodes
Elastic Kubernetes Service EKS- configure cluster auto scalar
Google Kubernetes Engine
GKE Auto Scalar
Auto scaling feature needs to be supported by the underlying cloud provider. Google cloud supports auto scaling during cluster creation or update by passing flags --enable-autoscaling --min-nodes and --max-nodes to the corresponding gcloud commands.
Examples:
gcloud container clusters create mytestcluster --zone=us-central1-b --enable-autoscaling --min-nodes=3 --max-nodes=10 --num-nodes=5
gcloud container clusters update mytestcluster --enable-autoscaling --min-nodes=1 --max-nodes=15
below link would be helpful
https://medium.com/kubecost/understanding-kubernetes-cluster-autoscaling-675099a1db92
I am exploring about implementation of Kubernetes cluster and deployment into Kubernetes cluster using Jenkins via CI/CD pipeline. When exploring I found that we don't need to define the worker machine node where we need to deploy our pods. Kubernetes master will take care for where to deploy / free pod in worker machine for deployment. We only need to define how much memory need to that pod in definition.
Here my confusion is that, Already we assigned and configured Kubernetes cluster for deployment. That all nodes containing its own memory according to creation of AWS EC2 (since I am planning to use AWS Ec2 - Ubuntu 16.04 LTS).
So why we again need to define memory in pod ? Is that proper way of pod deployment ?
I am only started in CI/CD pipeline world.
Specifying memory and cpu in the pod specification is completely optional. Still there are a couple of aspects to specifying memory and CPU at pod level:
As explained here, if you don't specify CPU/memory - the pod/container can consume all resources on that node and potentially affect other pod/containers running on that node.
Each application should specify the memory and CPU they need for running the application. This information is used by Kubernetes during scheduling the pod on one of the nodes in the cluster where enough resources are available. This information ensures better scheduling decisions.
It enables the Horizontal Pod Autoscaler (HPA) to scale the pods when the resource consumption beyond a certain limit. The details are explained in this doc. Unless there is a memory/cpu limit specified, you can not calculate that the pod is running 80% of that metric and it should be scaled into two replicas.
You can also enable a certain default at namespace level and then only override for specific applications, details here
I am using Google Container Engine . Now I want auto scaling functionality in my cluster . As per documentation GKE autoscaler is in beta release . I can also enable autoscaling in instance group that is managing cluster nodes .
Cluster autoscaler add/remove nodes so that all scheduled pods have a place to run where instance group add/remove nodes based on different policies like average cpu utilization .
I think by adjusting pods CPU limit and target CPU utilization for pods in Kubernetes autoscaler , Managed Instance Group autoscaling can also be used to resize GKE cluster .
So my question is what should I use ?
Short answer - don't use GCE MIG autoscaling feature. It will just not work properly with your cluster.
See details in this FAQ:
https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#should-i-use-a-cpu-usage-based-node-autoscaler-with-kubernetes
(read the question linked above and 2 next ones)
As per GCP docs :
"Caution: Do not enable Compute Engine autoscaling for managed instance groups for your cluster nodes. GKE's cluster autoscaler is separate from Compute Engine autoscaling. This can lead to node pools failing to scale up or scale down as the Compute Engine autoscaler will be in conflict with GKE's cluster autoscaler"
More Details :
https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler