Kubernetes resources under the hood — Part 2

Do you think that CPU requests are just used for scheduling? Think again. Introducing CPU Shares, and laying the grounds for removing your limits!

Shon Lev-Ran
Published in
7 min readAug 28, 2022


Co-Authored by@shirmon

Understanding CPU Requests

In the previous post, I talked about the foundation of Kubernetes resource management. In this post, we will dive deeper into what is going on behind the scenes when we configure CPU requests to a pod’s containers.

apiVersion: v1
kind: Pod
name: frontend
- name: app
image: images.my-company.example/app:v4
memory: "64Mi"
cpu: "250m"
memory: "128Mi"
cpu: "500m" # For the last time!

Resource requests are first and foremost used for scheduling decisions, but is there anything more to CPU requests?

CPU Shares

When you configure an X amount of vCPUs as a container CPU request in your pod’s manifest, Kubernetes configures (1024 * X) CPU shares for your container.

For example, if I configure 250m for my CPU requests Kubernetes will set 1024 * 250m = 256 CPU shares.

So what are CPU shares and what do they do?

To understand CPU shares, let’s talk first about the Kernel mechanism called CFS (Completely Fair Scheduler).

CFS — Completely Fair Scheduler

CFS is the default Linux CPU scheduler and is in charge of allocating CPU time between processes fairly.

The “completely fair” part is not as simple as it sounds, it uses a few parameters to decide what is the relative weight (priority) of each process. Many of you may be familiar with the “nice” setting that can be set for processes to change their relative weight. But currently, Kubernetes doesn’t use nice to affect the processes’ weight, instead, it configures CPU shares for a CGroup.

So, CPU shares are a Linux CGroup feature that is designed to prioritize CGroup processes for the CFS to allocate more CPU time at times of congestion to the higher priority processes.

Let me explain;

Let’s think of a single CPU timeframe (1 second for example) as a pizza. Every second a new pizza comes out of the oven, processes eat what they need from it, and then it’s gone. If all of my processes are not hungry enough to eat all the pizza in 1 second, they will eat their fill until the time is over and a new CPU-second-pizza will come out of the oven. Yummy! 🍕

CPU feeding Homer
Sufficient CPU per second

The complications start when our processes are hungry and 1 pizza every second is not enough to feed them.

Insufficient CPU per second

When there is not enough CPU time (or pizza) for all of my processes, CFS will look at the shares every CGroup has, will cut the pizza into the sum of all shares, and will split it accordingly.

In the case that many of the processes in the CGroup want more CPU than available, the slice that each CGroup receives will be evenly distributed between the processes in that CGroup.

So for example, if processes in 5 CGroups are requesting the maximum amount of CPU possible, and each of the CGroups has an equal amount of CPU shares, then the CPU time will be distributed evenly between the CGroups.

CPU shared between CGroups with the same amount of shares

Another example is (Staying in the state that all processes are requesting as much CPU as possible); if I have 3 CGroups with 1024 CPU shares each, and one other CGroup with 3072 shares the first 3 CGroups will get 1/6 of the CPU, and the last CGroup will get half (3/6)

CPU shared between CGroups with different amounts of shares

Remember, all of this only matters if I’m lacking CPU, if I have 3 CGroups with X CPU shares that need a lot of CPU and the fourth CGroup with 1000X CPU shares that is idle, the first 3 will split the CPU equally.

CPU shared only between hungry CGroups

Can my container even have 1,048,576 CPU shares on Kubernetes? Only if my node has more than 1024 CPU cores such as the Epiphany-V, but I’m sure most of us don’t have those kinds of nodes.

How Kubernetes uses these features

So as I’ve said, Kubernetes CPU requests configure CPU shares for our containers CGroups,

Shares “over-commitment” is prevented by Kubernetes magic; On one hand, the scheduler only schedules on each node the total amount of CPU requests to be lower or equal to the amount of CPU on the node (allocatable — see previous part). On the other hand, the CPU shares you provision can be up to 1024 times the number of cores. That sets a cap on the maximum number of shares that can be used by the pods, and the ratio remains.

The sum of CPU shares your containers can have on Kubernetes is 1024 times the number of allocatable CPUs you have in your cluster.

Real-life examples

I tried to make the previous examples as simple as possible so I removed some important parameters such as:

  • Threads and processes count in each CGroup
  • The CPU consumed by the node (other than your running pods)

There are some other parameters that don’t take effect although you might think so. Such as:

  • Quality of Service (QoS)
  • Pod priority
  • Evictions

Let’s have a shallow dive into them;

Thread Count

When we run just a single process in our container, if that process only creates a single thread, it can not consume more than one core anyway. When you set CPU requests to your containers, always bear in mind the number of threads they will run.

A side note — threads are not free, try not to use too many treads as each thread brings its own overhead, and increase the number of replicas instead.

Node Load

The bar charts from earlier are for isolated processes, but not all processes are isolated. Not to worry! The CGroups for your containers are pretty low on the CGroups hierarchy.

Simple Kubernetes CGroups map

Maybe while reading you already went to check how many CPU shares your Kubelet has to make sure it’s not deprived. Don’t worry, Your pods and containers are just sharing the CPU time “kubepods” CGroup is eligible for. If the Kubelet, the container runtime, or other services on the node need CPU time, they will get it.

Don’t worry when setting high CPU requests, the node’s components are higher priority out of the box.

Quality of Service

Kubernetes is configuring CGroups per QoS, currently, they have no real function and they exist for future use.

In terms of CPU time and priority, the CPU Request is the only thing that matters.

So what will happen if you don’t set CPU Requests? The container will get 2 CPU shares by default and will have a very low priority compared to pods that have CPU requests configured.

CPU time allocation will be the same both for burstable and best-effort pods. Guaranteed will have another parameter impacting the CPU time. More on that in the next part.
Bottom line is that the QoS doesn’t directly affect the CPU time a pod's containers will receive. The only thing matter is CPU shares (and limits if you still use them).

Pod Priority

“It’s OK, I set pod priority.” — Sorry but not exactly…

Pod priority is only used to determine the termination order on node evection, and as we’ve mentioned; There is no eviction caused by CPU pressure.


Eviction is a process running on the node that chooses and kills pods when the node is low on resources. Eviction only happens for in-compressible resources like memory, disk space, etc. more on that in the fourth part.

Not just for scheduling

We learned that CPU requests are used not only for scheduling purposes but also for the lifetime of the container. Memory requests also have their deep layers, more on that in part four.

Also, we talked only about normal Kubernetes behavior, there are many other options like CPU pinning that sets exclusive CPU cores per container. That’s outside of the scope of this article, but we may get into it in the future 😄

To summarize;

We learned that CPU requests are not used just for scheduling, but also take a huge part in the whole container lifecycle! We learned the importance of setting the correct requests to configure the right amount of CPU shares for each container and why configurations such as QoS don’t really affect our workloads.

Remember! CPU requests configure how much CPU will be Guaranteed to your container throughout its lifecycle!

Part 3 is out! Explains all about why you should remove your CPU limits.