Change:
// Its corresponding flag only gets registered in Windows builds. WindowsService bool <ins> // WindowsPriorityClass sets the priority class associated with the Kubelet process // Its corresponding flag only gets registered in Windows builds // The default priority class associated with any process in Windows is NORMAL_PRIORITY_CLASS. Keeping it as is // to maintain backwards compatibility. // Source: https://docs.microsoft.com/en-us/windows/win32/procthread/scheduling-priorities WindowsPriorityClass string </ins> // remoteRuntimeEndpoint is the endpoint of remote runtime service RemoteRuntimeEndpoint string // remoteImageEndpoint is the endpoint of remote image service

Discussion:
<!-- Please use this template while reporting a bug and provide as much info as possible. Not doing so may result in your bug not being addressed in a timely manner. Thanks! If the matter is security related, please disclose it privately via --What happened: Windows nodes running with high (90%) CPU usage are having calls to /stats/summary time out with "Context Deadline Exceeded". When this happens metrics-server cannot gather accurate CPU/memory usage for the node or pods running on the node which results in HPA not functioning returning <unknowninstead of values What you expected to happen: Windows nodes should be able to run with high CPU utilization without negatively impacting Kubernetes functionality How to reproduce it (as minimally and precisely as possible): a cluster with Windows Server 2019 nodes CPU intensive workloads into cluster ex: workload to put node into high CPU state node metrics with Anything else we need to know?: This issue appears to be easier to reproduce on 1.18.x nodes vs 1.16.x or 1.17.x nodes Ex: ! ! metrics-server logs show E1020 10:58:24. 1 ] unable to fully collect metrics: unable to fully scrape metrics from source kubeletsummary:akswin000000: unable to fetch metrics from Kubelet akswin000000 (10.240.0.115): Get https://10.240.0.115:/stats/summary?onlycpuandmemory=true: context deadline exceeded Kubelet logs show and Environment: Kubernetes version (use ): 1.18.8 Cloud provider or hardware configuration: AKS / Azure OS (e.g: ): WIndows Server 2019 Kernel (e.g. ): Install tools: Network plugin and version (if this is a network-related bug): Others: Docker EE version 19.3.11
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/sig windows /area kubelet
I was able to reproduce on 1.18.8 during a scale operation: where it fully subscribes all CPU's in Allocatable: Metric server logs: I do not see "context deadline" in kubelet but I did find some logs assocated: Once the pods are scheduled the node remains at 100% cpu and I no longer see errors but is not accurate: on the node (run after the pod deployment stabilized)
Adding to kubelete's flag and I no longer see the errors. This reserves some cpu for kubelet and other processes. We call this out in the but do not do the same for cpu.
Also, without setting system reserved CPU, it looks like does not accurately reflect CPU usage on the nodes when CPU utilization is very high. When I got some nodes into this state I did not see any error messages in logs from metrics-server and also I noticed that that querying the metrics API directly from showed recent timestamps.
I run into this again during scaling, collectiong logs for analysis here: Metric Server logs: Kubelet logs:
have you experienced this at all?
I believe has seen high CPU issues before. Not particularly on this. I've heard there were kernel issues in the past that triggered high CPU cases.
Some investigations on our end are showing that the DiagTrack service running in the infra/pause container can consume non-trivial amounts of CPU. I'll create an issue to address that problem as it might help out some here. That certainly will not eliminate the problem we are seeing tho.
is an issue for DiagTrack
The differences in behavior between 1.17. and 1.18. clusters looks like it is caused by (which was NOT backported to any prior releases) K8s version <= 1.17 behavior Prior to these changes CPU weights for containers were inferred based on limit values set in the pod specs. The observed behavior on Windows system when CPU weights were used was that under high-CPU load pods/containers were not guaranteed their scheduling time. The good news here is that system critical services like kubelet/docker/etc were still able to get enough CPU time for the nodes to function properly. K8s version = 1.18 behavior After these changes CPU limits were enforced for pods.

The observed behavior on 1.18 clusters is the when a Windows node enters a high-CPU state is that containers are guaranteed their scheduling time but host resources start to get starved. Usually this manifests as the node entering the state, metrics not working correctly (because calls to /stats/summary are timing out) or both. Suggestion / possible fixes Set the Windows process priority of the kubelet to  to help ensure it gets CPU time Kubelet can do this itself though calls (GetCurrentProcess() and Get/SetPriorityClass()) Set reserve CPU with with --system-reserves=[cpu=500m] This needs to scaled based on number of CPUs on host based on my experimentations Currently guidance on for Windows only mentions reserving system-memory It is still possible to over-commit CPU and starve kubelet/docker/HCS if users schedule pods with mis-matching requests and limits Update documentation.
Kubelet can do this itself though calls (GetCurrentProcess() and Get/SetPriorityClass()) I can work on this, I am wondering if we should do the same for CRI(like docker) and CNI processes.
Sure, if you want to work on this go ahead. I think we should probably try to make this be toggleable via a kubelet flag. I believe there are other windows specific flags already implemented in the kubelet (like one to run as a windows service IIRC)
docker/containerd should probably have a flag that allows them to run as high priory as well but would likely be different issues in the respective projects
/reopen I don't think this was ever fully addressed
Reopened this issue. <detailsIn response to : /reopen I don't think this was ever fully addressed Instructions for interacting with me using PR comments are available . If you have questions or suggestions related to my behavior, please file an issue against the repository. </details
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