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Configure Out Of Resource Handling

This page explains how to configure out of resource handling with kubelet.

The kubelet needs to preserve node stability when available compute resources are low. This is especially important when dealing with incompressible compute resources, such as memory or disk space. If such resources are exhausted, nodes become unstable.

Eviction Policy

The kubelet can proactively monitor for and prevent total starvation of a compute resource. In those cases, the kubelet can reclaim the starved resource by proactively failing one or more Pods. When the kubelet fails a Pod, it terminates all of its containers and transitions its PodPhase to Failed.

Eviction Signals

The kubelet supports eviction decisions based on the signals described in the following table. The value of each signal is described in the Description column, which is based on the kubelet summary API.

Eviction Signal Description
memory.available memory.available := node.status.capacity[memory] - node.stats.memory.workingSet
nodefs.available nodefs.available := node.stats.fs.available
nodefs.inodesFree nodefs.inodesFree := node.stats.fs.inodesFree
imagefs.available imagefs.available := node.stats.runtime.imagefs.available
imagefs.inodesFree imagefs.inodesFree := node.stats.runtime.imagefs.inodesFree

Each of the above signals supports either a literal or percentage based value. The percentage based value is calculated relative to the total capacity associated with each signal.

The value for memory.available is derived from the cgroupfs instead of tools like free -m. This is important because free -m does not work in a container, and if users use the node allocatable feature, out of resource decisions are made local to the end user Pod part of the cgroup hierarchy as well as the root node. This script reproduces the same set of steps that the kubelet performs to calculate memory.available. The kubelet excludes inactive_file (i.e. # of bytes of file-backed memory on inactive LRU list) from its calculation as it assumes that memory is reclaimable under pressure.

kubelet supports only two filesystem partitions.

  1. The nodefs filesystem that kubelet uses for volumes, daemon logs, etc.
  2. The imagefs filesystem that container runtimes uses for storing images and container writable layers.

imagefs is optional. kubelet auto-discovers these filesystems using cAdvisor. kubelet does not care about any other filesystems. Any other types of configurations are not currently supported by the kubelet. For example, it is not OK to store volumes and logs in a dedicated filesystem.

In future releases, the kubelet will deprecate the existing garbage collection support in favor of eviction in response to disk pressure.

Eviction Thresholds

The kubelet supports the ability to specify eviction thresholds that trigger the kubelet to reclaim resources.

Each threshold has the following form:

[eviction-signal][operator][quantity]

where:

For example, if a node has 10Gi of total memory and you want trigger eviction if the available memory falls below 1Gi, you can define the eviction threshold as either memory.available<10% or memory.available<1Gi. You cannot use both.

Soft Eviction Thresholds

A soft eviction threshold pairs an eviction threshold with a required administrator-specified grace period. No action is taken by the kubelet to reclaim resources associated with the eviction signal until that grace period has been exceeded. If no grace period is provided, the kubelet returns an error on startup.

In addition, if a soft eviction threshold has been met, an operator can specify a maximum allowed Pod termination grace period to use when evicting pods from the node. If specified, the kubelet uses the lesser value among the pod.Spec.TerminationGracePeriodSeconds and the max allowed grace period. If not specified, the kubelet kills Pods immediately with no graceful termination.

To configure soft eviction thresholds, the following flags are supported:

Hard Eviction Thresholds

A hard eviction threshold has no grace period, and if observed, the kubelet will take immediate action to reclaim the associated starved resource. If a hard eviction threshold is met, the kubelet kills the Pod immediately with no graceful termination.

To configure hard eviction thresholds, the following flag is supported:

The kubelet has the following default hard eviction threshold:

Eviction Monitoring Interval

The kubelet evaluates eviction thresholds per its configured housekeeping interval.

Node Conditions

The kubelet maps one or more eviction signals to a corresponding node condition.

If a hard eviction threshold has been met, or a soft eviction threshold has been met independent of its associated grace period, the kubelet reports a condition that reflects the node is under pressure.

The following node conditions are defined that correspond to the specified eviction signal.

Node Condition Eviction Signal Description
MemoryPressure memory.available Available memory on the node has satisfied an eviction threshold
DiskPressure nodefs.available, nodefs.inodesFree, imagefs.available, or imagefs.inodesFree Available disk space and inodes on either the node’s root filesytem or image filesystem has satisfied an eviction threshold

The kubelet continues to report node status updates at the frequency specified by --node-status-update-frequency which defaults to 10s.

Oscillation of node conditions

If a node is oscillating above and below a soft eviction threshold, but not exceeding its associated grace period, it would cause the corresponding node condition to constantly oscillate between true and false, and could cause poor scheduling decisions as a consequence.

To protect against this oscillation, the following flag is defined to control how long the kubelet must wait before transitioning out of a pressure condition.

The kubelet would ensure that it has not observed an eviction threshold being met for the specified pressure condition for the period specified before toggling the condition back to false.

Reclaiming node level resources

If an eviction threshold has been met and the grace period has passed, the kubelet initiates the process of reclaiming the pressured resource until it has observed the signal has gone below its defined threshold.

The kubelet attempts to reclaim node level resources prior to evicting end-user Pods. If disk pressure is observed, the kubelet reclaims node level resources differently if the machine has a dedicated imagefs configured for the container runtime.

With imagefs

If nodefs filesystem has met eviction thresholds, kubelet frees up disk space by deleting the dead Pods and their containers.

If imagefs filesystem has met eviction thresholds, kubelet frees up disk space by deleting all unused images.

Without imagefs

If nodefs filesystem has met eviction thresholds, kubelet frees up disk space in the following order:

  1. Delete dead Pods and their containers
  2. Delete all unused images

Evicting end-user Pods

If the kubelet is unable to reclaim sufficient resource on the node, kubelet begins evicting Pods.

The kubelet ranks Pods for eviction first by their quality of service, and then by the consumption of the starved compute resource relative to the Pods’ scheduling requests.

As a result, kubectl ranks and evicts Pods in the following order:

If necessary, kubelet evicts Pods one at a time to reclaim disk when DiskPressure is encountered. If the kubelet is responding to inode starvation, it reclaims inodes by evicting Pods with the lowest quality of service first. If the kubelet is responding to lack of available disk, it ranks Pods within a quality of service that consumes the largest amount of disk and kill those first.

With imagefs

If nodefs is triggering evictions, kubelet sorts Pods based on the usage on nodefs

If imagefs is triggering evictions, kubelet sorts Pods based on the writable layer usage of all its containers.

Without imagefs

If nodefs is triggering evictions, kubelet sorts Pods based on their total disk usage

Minimum eviction reclaim

In certain scenarios, eviction of Pods could result in reclamation of small amount of resources. This can result in kubelet hitting eviction thresholds in repeated successions. In addition to that, eviction of resources like disk, is time consuming.

To mitigate these issues, kubelet can have a per-resource minimum-reclaim. Whenever kubelet observes resource pressure, kubelet attempts to reclaim at least minimum-reclaim amount of resource below the configured eviction threshold.

For example, with the following configuration:

--eviction-hard=memory.available<500Mi,nodefs.available<1Gi,imagefs.available<100Gi
--eviction-minimum-reclaim="memory.available=0Mi,nodefs.available=500Mi,imagefs.available=2Gi"`

If an eviction threshold is triggered for memory.available, the kubelet works to ensure that memory.available is at least 500Mi. For nodefs.available, the kubelet works to ensure that nodefs.available is at least 1.5Gi, and for imagefs.available it works to ensure that imagefs.available is at least 102Gi before no longer reporting pressure on their associated resources.

The default eviction-minimum-reclaim is 0 for all resources.

Scheduler

The node reports a condition when a compute resource is under pressure. The scheduler views that condition as a signal to dissuade placing additional pods on the node.

Node Condition Scheduler Behavior
MemoryPressure No new BestEffort Pods are scheduled to the node.
DiskPressure No new Pods are scheduled to the node.

Node OOM Behavior

If the node experiences a system OOM (out of memory) event prior to the kubelet is able to reclaim memory, the node depends on the oom_killer to respond.

The kubelet sets a oom_score_adj value for each container based on the quality of service for the Pod.

Quality of Service oom_score_adj
Guaranteed -998
BestEffort 1000
Burstable min(max(2, 1000 - (1000 * memoryRequestBytes) / machineMemoryCapacityBytes), 999)

If the kubelet is unable to reclaim memory prior to a node experiencing system OOM, the oom_killer calculates an oom_score based on the percentage of memory it’s using on the node, and then add the oom_score_adj to get an effective oom_score for the container, and then kills the container with the highest score.

The intended behavior should be that containers with the lowest quality of service that are consuming the largest amount of memory relative to the scheduling request should be killed first in order to reclaim memory.

Unlike Pod eviction, if a Pod container is OOM killed, it may be restarted by the kubelet based on its RestartPolicy.

Best Practices

The following sections describe best practices for out of resource handling.

Schedulable resources and eviction policies

Consider the following scenario:

To facilitate this scenario, the kubelet would be launched as follows:

--eviction-hard=memory.available<500Mi
--system-reserved=memory=1.5Gi

Implicit in this configuration is the understanding that “System reserved” should include the amount of memory covered by the eviction threshold.

To reach that capacity, either some Pod is using more than its request, or the system is using more than 500Mi.

This configuration ensures that the scheduler does not place Pods on a node that immediately induce memory pressure and trigger eviction assuming those Pods use less than their configured request.

DaemonSet

It is never desired for kubelet to evict a DaemonSet Pod, since the Pod is immediately recreated and rescheduled back to the same node.

At the moment, the kubelet has no ability to distinguish a Pod created from DaemonSet versus any other object. If/when that information is available, the kubelet could pro-actively filter those Pods from the candidate set of Pods provided to the eviction strategy.

In general, it is strongly recommended that DaemonSet not create BestEffort Pods to avoid being identified as a candidate Pod for eviction. Instead DaemonSet should ideally launch Guaranteed Pods.

Deprecation of existing feature flags to reclaim disk

kubelet has been freeing up disk space on demand to keep the node stable.

As disk based eviction matures, the following kubelet flags are marked for deprecation in favor of the simpler configuration supported around eviction.

Existing Flag New Flag
--image-gc-high-threshold --eviction-hard or eviction-soft
--image-gc-low-threshold --eviction-minimum-reclaim
--maximum-dead-containers deprecated
--maximum-dead-containers-per-container deprecated
--minimum-container-ttl-duration deprecated
--low-diskspace-threshold-mb --eviction-hard or eviction-soft
--outofdisk-transition-frequency --eviction-pressure-transition-period

Known issues

The following sections describe known issues related to out of resource handling.

kubelet may not observe memory pressure right away

The kubelet currently polls cAdvisor to collect memory usage stats at a regular interval. If memory usage increases within that window rapidly, the kubelet may not observe MemoryPressure fast enough, and the OOMKiller will still be invoked. We intend to integrate with the memcg notification API in a future release to reduce this latency, and instead have the kernel tell us when a threshold has been crossed immediately.

If you are not trying to achieve extreme utilization, but a sensible measure of overcommit, a viable workaround for this issue is to set eviction thresholds at approximately 75% capacity. This increases the ability of this feature to prevent system OOMs, and promote eviction of workloads so cluster state can rebalance.

kubelet may evict more Pods than needed

The Pod eviction may evict more Pods than needed due to stats collection timing gap. This can be mitigated by adding the ability to get root container stats on an on-demand basis (https://github.com/google/cadvisor/issues/1247) in the future.

How kubelet ranks Pods for eviction in response to inode exhaustion

At this time, it is not possible to know how many inodes were consumed by a particular container. If the kubelet observes inode exhaustion, it evicts Pods by ranking them by quality of service. The following issue has been opened in cadvisor to track per container inode consumption (https://github.com/google/cadvisor/issues/1422) which would allow us to rank Pods by inode consumption. For example, this would let us identify a container that created large numbers of 0 byte files, and evict that Pod over others.

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