I am using Openshift 4, CPU Request: 0.2, Limit 0.4.
From the monitoring, I can see the CPU usage started from 0.1, and increased gradually. Is it because that there is a machanisim to prevent over reserve the CPU usage?
Can I setup that the pod to use the max request CPU from the beginning, and adapt to Limit as fast as possible?
The max limit is already available from the beginning (presuming that the node has the CPU available to give). OCP is using CFS to enforce that limit, and CFS doesn't have anything that gradually kicks in, CFS only has one thing it considers: the configured limit.
As for why you are seeing this in your monitoring, I'm not sure. But my first guess would be that that graph is using a moving average. (And thus, since it's a moving average it will converge towards the actual usage.)
Related
I'm trying to understand how to compute the CPU utilisation for audio and video use cases.
In real time audio applications, this is what I typically do:
if an application takes 4ms to process 28ms of audio data, I say that the CPU utilisation is 14.28% (4/28).
How should this be done for applications like resize/crop? let's say I'm resizing an image from 162*122 to 128*128 size image at 1FPS, and it takes 11ms.. What would be the CPU utilisation?
CPU utilization is quite complicated, and strongly depends on stuff like:
The CPU itself
The algorithms utilized for the task
Other tasks running alongside the CPU
CPU utilization is also strongly related to the process scheduling of your PC, hence the operating system used, so most operating systems will expose some kind of API for CPU utilization diagnostics, but such API is highly platform-dependent.
But how does CPU utilization calculations work anyway?
The most simple way in which CPU utilization is calculated is taking a (for example) 1 second period, in which you observe how long the CPU has been idling (not executing any processes), and divide that by the time interval you selected. For example, if the CPU did useful calculations for 10 milliseconds, and you were observing for 500ms, this would mean that the CPU utilization is 2%.
Answering your question / TL; DR
You can apply this principle in your program. For the case you provided (processing video), this could be done in more or less the same way: you calculate how long it takes to calculate one frame, and divide that by the length of a frame (1 / FPS). Of course, this could be done for a longer period of time, to get a more accurate reading, in the following way: you track how much time it takes to process, for example, 2 seconds of video, and divide that by 2. Then, you'll have your CPU utilization.
NOTE: if you aren't able to process the frame in time, for example, your video is 10FPS (0.1ms), and processing one frame takes 0.5ms, then your CPU utilization will be seemingly 500%, but obviously you can't utilize more than 100% of your CPU, so you should just cap the CPU utilization at 100%.
I’d like to autoscale the pods not based on the average memory, but rather based on largest amount of available memory in a given pod.
Example:
Let’s say the target maximum available memory is 50%.
If we have 7 pods already and 6 of them have 90% occupied memory, but a single pod with 40% occupied memory, that’d satisfy my criteria and we won’t need to upscale. But the moment that last pod goes below 50% available memory we’ll upscale.
I know it’s not a wise criteria for scaling in case majority of case, but in my particular circumstance, it fits.
Let's take this processor as an example: a CPU with 2 cores and 4 threads (2 threads per core).
From what I've read, such a CPU has 2 physical cores but can process 4 threads simultaneously through hyper threading. But, in reality, one physical core can only truly run one thread at a time, but using hyper threading, the CPU exploits the idle stages in the pipeline to process another thread.
Now, here is Kubernetes with Prometheus and Grafana and their CPU resource units measurement - millicore/millicpu. So, they virtually slice a core to 1000 millicores.
Taking into account the hyper threading, I can't understand how they calculate those millicores under the hood.
How can a process, for example, use 100millicore (10th part of the core)? How is this technically possible?
PS: accidentally, found a really descriptive explanation here: Multi threading with Millicores in Kubernetes
This gets very complicated. So k8s doesn't actually manage this it just provides a layer on top of the underlying container runtime (docker, containerd etc). When you configure a container to use 100 millicore k8's hands that down to the underlying container runtime and the runtime deals with it. Now once you start going to this level you have to start looking at the Linux kernel and how it does cpu scheduling / rate with cgroups. Which becomes incredibly interesting and complicated. In a nutshell though: The linux CFS Bandwidth Control is the thing that manages how much cpu a process (container) can use. By setting the quota and period params to the schedular you can control how much CPU is used by controlling how long a process can run before being paused and how often it runs. as you correctly identify you cant only use a 10th of a core. But you can use a 10th of the time and by doing that you can only use a 10th of the core over time.
For example
if I set quota to 250ms and period to 250ms. That tells the kernel that this cgroup can use 250ms of CPU cycle time every 250ms. Which means it can use 100% of the CPU.
if I set quota to 500ms and keep the period to 250ms. That tells the kernel that this cgroup can use 500ms of CPU cycle time every 250ms. Which means it can use 200% of the CPU. (2 cores)
if I set quota to 125ms and keep the period to 250ms. That tells the kernel that this cgroup can use 125ms of CPU cycle time every 250ms. Which means it can use 50% of the CPU.
This is a very brief explanation. Here is some further reading:
https://blog.krybot.com/a?ID=00750-cfae57ed-c7dd-45a2-9dfa-09d42b7bd2d7
https://www.kernel.org/doc/html/latest/scheduler/sched-bwc.html
I have a service deployed in Kubernetes and I am trying to optimize the requested cpu resources.
For now, I have deployed 10 instances and set spec.containers[].resources.limits.cpu to 0.1, based on the "average" use. However, it became obvious that this average is rather useless in practice because under constant load, the load increases significantly (to 0.3-0.4 as far as I can tell).
What happens consequently, when multiple instances are deployed on the same node, is that this node is heavily overloaded; pods are no longer responsive, are killed and restarted etc.
What is the best practice to find a good value? My current best guess is to increase the requested cpu to 0.3 or 0.4; I'm looking at Grafana visualizations and see that the pods on the heavily loaded node(s) converge there under continuous load.
However, how can I know if they would use more load if they could before becoming unresponsive as the node is overloaded?
I'm actually trying to understand how to approach this in general. I would expect an "ideal" service (presuming it is CPU-focused) to use close to 0.0 when there is no load, and close to 1.0 when requests are constantly coming in. With that assumption, should I set the cpu.requests to 1.0, taking a perspective where actual constant usage is assumed?
I have read some Kubernetes best practice guides, but none of them seem to address how to set the actual value for cpu requests in practice in more depth than "find an average".
Basically come up with a number that is your lower acceptable bound for how much the process runs. Setting a request of 100m means that you are okay with a lower limit of your process running 0.1 seconds for every 1 second of wall time (roughly). Normally that should be some kind of average utilization, usually something like a P99 or P95 value over several days or weeks. Personally I usually look at a chart of P99, P80, and P50 (median) over 30 days and use that to decide on a value.
Limits are a different beast, they are setting your CPU timeslice quota. This subsystem in Linux has some persistent bugs so unless you've specifically vetted your kernel as correct, I don't recommend using it for anything but the most hostile of programs.
In a nutshell: Main goal is to understand how much traffic a pod can handle and how much resource it consumes to do so.
CPU limits are hard to understand and can be harmful, you might want
to avoid them, see static policy documentation and relevant
github issue.
To dimension your CPU requests you will want to understand first how much a pod can consume during high load. In order to do this you can :
disable all kind of autoscaling (HPA, vertical pod autoscaler, ...)
set the number of replicas to one
lift the CPU limits
request the highest amount of CPU you can on a node (3.2 usually on 4cpu nodes)
send as much traffic as you can on the application (you can achieve simple Load Tests scenarios with locust for example)
You will eventually end up with a ratio clients-or-requests-per-sec/cpu-consumed. You can suppose the relation is linear (this might not be true if your workload complexity is O(n^2) with n the number of clients connected, but this is not the nominal case).
You can then choose the pod resource requests based on the ratio you measured. For example if you consume 1.2 cpu for 1000 requests per second you know that you can give each pod 1 cpu and it will handle up to 800 requests per second.
Once you know how much a pod can consume under its maximal load, you can start setting up cpu-based autoscaling, 70% is a good first target that can be refined if you encounter issues like latency or pods not autoscaling fast enough. This will avoid your nodes to run out of cpu if the load increases.
There are a few gotchas, for example single-threaded applications are not able to consume more than a cpu. Thus if you give it 1.5 cpu it will run out of cpu but you won't be able to visualize it from metrics as you'll believe it still can consume 0.5 cpu.
Horizontal scaling means that we scale by adding more machines into the pool of resources. Still, there is a choice of how much power (CPU, RAM) each node in the cluster will have.
When cluster managed with Kubernetes it is extremely easy to set any CPU and memory limit for Pods. How to choose the optimal CPU and memory size for cluster nodes (or Pods in Kubernetes)?
For example, there are 3 nodes in a cluster with 1 vCPU and 1GB RAM each. To handle more load there are 2 options:
Add the 4th node with 1 vCPU and 1GB RAM
Add to each of the 3 nodes more power (e.g. 2 vCPU and 2GB RAM)
A straightforward solution is to calculate the throughput and cost of each option and choose the cheaper one. Are there any more advanced approaches for choosing the compute resources of the nodes in a cluster with horizontal scalability?
For this particular example I would go for 2x vCPU instead of another 1vCPU node, but that is mainly cause I believe running OS for anything serious on a single vCPU is just wrong. System to behave decently needs 2+ cores available, otherwise it's too easy to overwhelm that one vCPU and send the node into dust. There is no ideal algorithm for this though. It will depend on your budget, on characteristics of your workloads etc.
As a rule of thumb, don't stick to too small instances as you have a bunch of stuff that has to run on them always, regardless of their size and the more node, the more overhead. 3x 4vCpu+16/32GB RAM sounds like nice plan for starters, but again... it depends on what you want, need and can afford.
The answer is related to such performance metrics as latency and throughput:
Latency is a time interval between sending request and receiving response.
Throughput is a request processing rate (requests per second).
Latency has influence on throughput: bigger latency = less throughput.
If a business transaction consists of multiple sequential calls of the services that can't be parallelized, then compute resources (CPU and memory) has to be chosen based on the desired latency value. Adding more instances of the services (horizontal scaling) will not have any positive influence on the latency in this case.
Adding more instances of the service increases throughput allowing to process more requests in parallel (if there are no bottlenecks).
In other words, allocate CPU and memory resources so that service has desired response time and add more service instances (scale horizontally) to handle more requests in parallel.