Resource Management for Windows nodes

This page outlines the differences in how resources are managed between Linux and Windows.

On Linux nodes, cgroups are used as a pod boundary for resource control. Containers are created within that boundary for network, process and file system isolation. The Linux cgroup APIs can be used to gather CPU, I/O, and memory use statistics.

In contrast, Windows uses a job object per container with a system namespace filter to contain all processes in a container and provide logical isolation from the host. (Job objects are a Windows process isolation mechanism and are different from what Kubernetes refers to as a Job).

There is no way to run a Windows container without the namespace filtering in place. This means that system privileges cannot be asserted in the context of the host, and thus privileged containers are not available on Windows. Containers cannot assume an identity from the host because the Security Account Manager (SAM) is separate.

Memory reservations

Windows does not have an out-of-memory process killer as Linux does. Windows always treats all user-mode memory allocations as virtual, and pagefiles are mandatory.

Windows nodes do not overcommit memory for processes running in containers. The net effect is that Windows won’t reach out of memory conditions the same way Linux does, and processes page to disk instead of being subject to out of memory (OOM) termination. If memory is over-provisioned and all physical memory is exhausted, then paging can slow down performance.

You can place bounds on memory use for workloads using the kubelet parameters --kubelet-reserve and/or --system-reserve; these account for memory usage on the node (outside of containers), and reduce NodeAllocatable. As you deploy workloads, set resource limits on containers. This also subtracts from NodeAllocatable and prevents the scheduler from adding more pods once a node is full.

Note: When you set memory resource limits for Windows containers, you should either set a limit and leave the memory request unspecified, or set the request equal to the limit.

On Windows, good practice to avoid over-provisioning is to configure the kubelet with a system reserved memory of at least 2GiB to account for Windows, Kubernetes and container runtime overheads.

CPU reservations

To account for CPU use by the operating system, the container runtime, and by Kubernetes host processes such as the kubelet, you can (and should) reserve a percentage of total CPU. You should determine this CPU reservation taking account of to the number of CPU cores available on the node. To decide on the CPU percentage to reserve, identify the maximum pod density for each node and monitor the CPU usage of the system services running there, then choose a value that meets your workload needs.

You can place bounds on CPU usage for workloads using the kubelet parameters --kubelet-reserve and/or --system-reserve to account for CPU usage on the node (outside of containers). This reduces NodeAllocatable. The cluster-wide scheduler then takes this reservation into account when determining pod placement.

On Windows, the kubelet supports a command-line flag to set the priority of the kubelet process: --windows-priorityclass. This flag allows the kubelet process to get more CPU time slices when compared to other processes running on the Windows host. More information on the allowable values and their meaning is available at Windows Priority Classes. To ensure that running Pods do not starve the kubelet of CPU cycles, set this flag to ABOVE_NORMAL_PRIORITY_CLASS or above.