I am trying to find the cpu requested by kubernetes pods rolled up to an hour.
lets say we have an average cluster cpu allocatable as 10 cpu cores
pod 1 requests 5 cpu for 20 mins.
pod 2 requests 5 cpu for 30 mins.
pod 3 requests 5 cpu for 1 hr.
now i have to find for an hour
how much cpu is occupied by pod 1 for that hour and likewise for pod 2 and 3.
since pod 3 is occupies 5 cpu thoroughout that hour - we can say pod 3 cpu occupied = 5
How to calculate the cpu requested by pod 1 and 2 normalized for that hour
my initial thought for normalizing was
For pod 1 => 5*(20/60) = 1.666667 (requests normalized for an hour)
But when i use it for the metrics
i see sum(pod1+pod2+....+podn) > total cluster cpu -- like way higher( 340 > 80)
Any thoughts on logic here?
Does Kubernetes always allocate the pod requests ?
Related
It may sound like a naive question, I am running some load testing on one of the deployments on k8s. So to get an idea of the CPU utilization, I opened LENS HPA and CPU utilization is being shown like this
can anyone please tell me how to understand this number, earlier it was 380/50% for CPU.
I just want to get an idea of what does this number means, if it is 380/50, is my CPU not big enough?
It means probably the same as the output from the kubectl describe hpa {hpa-name}:
$ kubectl describe hpa php-apache
Name: php-apache
...
Metrics: ( current / target )
resource cpu on pods (as a percentage of request): 60% (120m) / 50%
It means that CPU has consumption increased to to x % of the request - good example and explanation in the Kubernetes docs:
Within a minute or so, you should see the higher CPU load; for example:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 305% / 50% 1 10 1 3m
and then, more replicas. For example:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 305% / 50% 1 10 7 3m
Here, CPU consumption has increased to 305% of the request.
So in your example (380%/50%) it means that you setup HPA to maintain an average CPU utilization across pods to 50% (by increasing and decreasing number of replicas - updating the deployment) and CPU consumption has increased to 380% so the deployment will be resized automatically.
Also check:
Horizontal Pod Autoscaling
HorizontalPodAutoscaler Walkthrough
I hope you can shed some light on this.
I am facing the same issue as described here: Kubernetes deployment not scaling down even though usage is below threshold
My configuration is almost identical.
I have checked the hpa algorithm, but I cannot find an explanation for the fact that I am having only one replica of my-app3.
Any hints?
kubectl get hpa -A
NAMESPACE NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
my-ns1 my-app1 Deployment/my-app1 49%/75%, 2%/75% 1 10 2 20h
my-ns2 my-app2 Deployment/my-app2 50%/75%, 10%/75% 1 10 2 22h
my-ns2 my-app3 Deployment/my-app3 47%/75%, 10%/75% 1 10 1 22h
kubectl top po -A
NAMESPACE NAME CPU(cores) MEMORY(bytes)
my-ns1 pod-app1-8d694bc8f-mkbrh 1m 76Mi
my-ns1 pod-app1-8d694bc8f-qmlnw 1m 72Mi
my-ns2 pod-app2-59d895d96d-86fgm 1m 77Mi
my-ns2 pod-app2-59d895d96d-zr67g 1m 73Mi
my-ns2 pod-app3-6f8cbb68bf-vdhsd 1m 47Mi
Posting this answer as it could be beneficiary for the community members on why exactly Horizontal Pod Autoscaler decided not to scale the amount of replicas in this particular setup.
The formula for amount of replicas workload will have is:
desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]
Following on the describe of HPA:
NAMESPACE NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
my-ns1 my-app1 Deployment/my-app1 49%/75%, 2%/75% 1 10 2 20h
my-ns2 my-app2 Deployment/my-app2 50%/75%, 10%/75% 1 10 2 22h
my-ns2 my-app3 Deployment/my-app3 47%/75%, 10%/75% 1 10 1 22h
HPA decides on the amount of replicas on the premise of their current amount.
A side note: In the setup that uses multiple metrics (for example CPU and RAM) it will use the higher metric and act accordingly.
Also please consider that downscaling has a cooldown.
Calculation on each of the Deployments
ceil[] - round a number up:
ceil(4,55) = 5
ceil(4,01) = 5
app1:
Replicas = ceil[2 * (49 / 75)]
Replicas = ceil[2 * 0,6533..]
Replicas = ceil[1,3066..]
Replicas = 2
This example shows that there will be no changes to be amount of replicas.
Amount of replicas would go:
Up when the currentMetricValue (49) would exceed the desiredMetricValue (75)
Down when the currentMetricValue (49) would be less than half of the desiredMetricValue (75)
app2 is in the same situation as app1 so it can be skipped
app3:
Replicas = ceil[1 * (49 / 75)]
Replicas = ceil[1 * 0,6266..]
Replicas = ceil[0,6266..]
Replicas = 1
This example also shows that there will be no changes to be amount of replicas.
Amount of replicas would go:
Up when the currentMetricValue (47) would exceed the desiredMetricValue (75)
Additional resources:
Kubernetes.io: Docs: Tasks: Run application: Horizontal pod autoscale
Indeed from my research it seems that the HPA algorithm works in this way:
https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#algorithm-details
Do not know the reason why my-app3 was assigned one replica and the other two apps two replicas, but according to the algorithm it is not needed to scale out at this time.
I set up my cluster and I want my deployments to scale up when the first pod uses 75% of one cpu (core). I did this with hpa and everything is working but I noticed that the hpa percentage is strange.
Based on what I know 1 cpu = 1000 milliunits and what I see in kubectl top pods is pod-A using 9m but what I see in kubectl get hpa is pod-A 9%/75% which doesn't make sense, 9% of 1000 is 90 and not 9.
I want to know how hpa is calculating the percentage and how shall I configure it so when I reach 75% of one cpu it scales up?
To the horizontal pod autoscaler 100% of a metric (cpu or memory) is the amount set in resource requests. So if you pod requests 100m cpu, 9m is 9% and it would scale out on 75m.
Double check if you really have requested 1 (or 1000m) cpu by issuing kubectl describe pod <pod-name>.
Kubernetes allows to limit pod resource usage.
requests:
cpu: 100m
memory: 128Mi
limits:
cpu: 200m # which is 20% of 1 core
memory: 256Mi
Let's say my kubernetes node has 2 core. And I run this pod with limit of CPU: 200m on this node. In this case, will my pod use it's underlying node's 1Core's 200m or 2Core's 100m+100m?
This calculation is needed for my gunicorn worker's number formula, or nginx worker's number etc..
In gunicorn documentation it says
Generally we recommend (2 x $num_cores) + 1 as the number of workers
to start off with.
So should I use 5 workers? (my node has 2 cores). Or it doesn't even matter since my pod has only allocated 200m cpu and I should consider my pod has 1 core?
TLDR: How many cores do pods use when its cpu usage is limited by kubernetes? If I run top inside pod, I'm seeing 2 cores available. But I'm not sure my application is using this 2 core's 10%+10% or 1core's 20%..
It will limit to 20% of one core, i.e. 200m. Also, limit means a pod can touch a maximum of that much CPU and no more. So pod CPU utilization will not always touch the limit.
Total CPU limit of a cluster is the total amount of cores used by all nodes present in cluster.
If you have a 2 node cluster and the first node has 2 cores and second node has 1 core, K8s CPU capacity will be 3 cores (2 core + 1 core). If you have a pod which requests 1.5 cores, then it will not be scheduled to the second node, as that node has a capacity of only 1 core. It will instead be scheduled to first node, since it has 2 cores.
CPU is measured in units called millicores. Each node in the cluster introspects the operating system to determine the amount of CPU cores on the node and then multiples that value by 1000 to express its total capacity. For example, if a node has 2 cores, the node’s CPU capacity would be represented as 2000m. If you wanted to use a 1/10 of a single core, you would represent that as 100m.
So, if in your cluster you provided 200m milicores, then it will stick to one core and take up the 20 percent of that core. Now if you provided another pod with 1.5m, then only it will take up more than one core.
I'm defining this autoscaler with kubernetes and GCE and I'm wondering what exactly should I specify for targetCPUUtilizationPercentage. That target points to what exactly? Is it the total CPU in my cluster? When the pods referenced in this autoscaler consume more than targetCPUUtilizationPercentage what happens?
The CPU utilization is the average CPU usage of a all pods in a deployment across the last minute divided by the requested CPU of this deployment. If the mean of the pods' CPU utilization is higher than the target you defined, the your replicas will be adjusted.
You can read more about this topic here.
This is average cpu utilisation of all the pods, so if you have given CPU as 200 in the resource requests and targetCPUUtilizationPercentage as 80%, then at 160 value of threshold, it will scale out the pod. It will create a new repliace.