node-feature-discovery/README.md

Node feature discovery for Kubernetes

• Overview
• Command line interface
• Feature discovery
  • Feature sources
  • Feature labels
• Getting started
  • System requirements
  • Usage
• Building from source
• Targeting nodes with specific features
• References
• License
• Demo

Overview

This software enables node feature discovery for Kubernetes. It detects hardware features available on each node in a Kubernetes cluster, and advertises those features using node labels.

This project uses GitHub milestones for release planning.

Command line interface

To try out stand-alone, one can run a Docker container where node-feature-discovery is already set as entry point. Such run is useful for checking features-detection part, but labeling part is expected to fail. It is recommended to use --no-publish and --oneshot to achieve clean run in stand-alone case.

node-feature-discovery.

  Usage:
  node-feature-discovery [--no-publish] [--sources=<sources>] [--label-whitelist=<pattern>]
     [--oneshot | --sleep-interval=<seconds>] [--config=<path>]
     [--options=<config>]
  node-feature-discovery -h | --help
  node-feature-discovery --version

  Options:
  -h --help                   Show this screen.
  --version                   Output version and exit.
  --config=<path>             Config file to use.
                              [Default: /etc/kubernetes/node-feature-discovery/node-feature-discovery.conf]
  --options=<config>          Specify config options from command line. Config
                              options are specified in the same format as in the
                              config file (i.e. json or yaml). These options
                              will override settings read from the config file.
                              [Default: ]
  --sources=<sources>         Comma separated list of feature sources.
                              [Default: cpu,cpuid,iommu,kernel,local,memory,network,pci,pstate,rdt,storage,system]
  --no-publish                Do not publish discovered features to the
                              cluster-local Kubernetes API server.
  --label-whitelist=<pattern> Regular expression to filter label names to
                              publish to the Kubernetes API server. [Default: ]
  --oneshot                   Label once and exit.
  --sleep-interval=<seconds>  Time to sleep between re-labeling. Non-positive
                              value implies no re-labeling (i.e. infinite
                              sleep). [Default: 60s]

NOTE Some feature sources need certain directories and/or files from the host mounted inside the NFD container. Thus, you need to provide Docker with the correct --volume options in order for them to work correctly when run stand-alone directly with docker run. See the template spec for up-to-date information about the required volume mounts.

Feature discovery

Feature sources

The current set of feature sources are the following:

• CPU
• CPUID for x86/Arm64 CPU details
• IOMMU
• Kernel
• Local (user-specific features)
• Memory
• Network
• Pstate (Intel P-State driver)
• RDT (Intel Resource Director Technology)
• Storage
• System

Feature labels

The published node labels encode a few pieces of information:

• Namespace, i.e. feature.node.kubernetes.io
• The source for each label (e.g. cpuid).
• The name of the discovered feature as it appears in the underlying source, (e.g. AESNI from cpuid).
• The value of the discovered feature.

Feature label names adhere to the following pattern:

<namespace>/<source name>-<feature name>[.<attribute name>]

The last component (i.e. attribute-name) is optional, and only used if a feature logically has sub-hierarchy, e.g. sriov.capable and sriov.configure from the network source.

Note: only features that are available on a given node are labeled, so the only label value published for features is the string "true".

{
  "feature.node.kubernetes.io/cpu-<feature-name>": "true",
  "feature.node.kubernetes.io/cpuid-<feature-name>": "true",
  "feature.node.kubernetes.io/iommu-<feature-name>": "true",
  "feature.node.kubernetes.io/kernel-<feature name>": "<feature value>",
  "feature.node.kubernetes.io/memory-<feature-name>": "true",
  "feature.node.kubernetes.io/network-<feature-name>": "true",
  "feature.node.kubernetes.io/pci-<device label>.present": "true",
  "feature.node.kubernetes.io/pstate-<feature-name>": "true",
  "feature.node.kubernetes.io/rdt-<feature-name>": "true",
  "feature.node.kubernetes.io/storage-<feature-name>": "true",
  "feature.node.kubernetes.io/system-<feature name>": "<feature value>",
  "feature.node.kubernetes.io/<hook name>-<feature name>": "<feature value>"
}

The --sources flag controls which sources to use for discovery.

Note: Consecutive runs of node-feature-discovery will update the labels on a given node. If features are not discovered on a consecutive run, the corresponding label will be removed. This includes any restrictions placed on the consecutive run, such as restricting discovered features with the --label-whitelist option.

CPU Features

The CPU feature source differs from the CPUID feature source in that it discovers CPU related features that are actually enabled, whereas CPUID only reports supported CPU capabilities (i.e. a capability might be supported but not enabled) as reported by the cpuid instruction.

Feature name	Description
hardware_multithreading	Hardware multithreading, such as Intel HTT, enabled (number of locical CPUs is greater than physical CPUs)

X86 CPUID Features (Partial List)

Feature name	Description
ADX	Multi-Precision Add-Carry Instruction Extensions (ADX)
AESNI	Advanced Encryption Standard (AES) New Instructions (AES-NI)
AVX	Advanced Vector Extensions (AVX)
AVX2	Advanced Vector Extensions 2 (AVX2)
BMI1	Bit Manipulation Instruction Set 1 (BMI)
BMI2	Bit Manipulation Instruction Set 2 (BMI2)
SSE4.1	Streaming SIMD Extensions 4.1 (SSE4.1)
SSE4.2	Streaming SIMD Extensions 4.2 (SSE4.2)
SGX	Software Guard Extensions (SGX)

Arm64 CPUID Features (Partial List)

Feature name	Description
AES	Announcing the Advanced Encryption Standard
EVSTRM	Event Stream Frequency Features
FPHP	Half Precision(16bit) Floating Point Data Processing Instructions
ASIMDHP	Half Precision(16bit) Asimd Data Processing Instructions
ATOMICS	Atomic Instructions to the A64
ASIMRDM	Support for Rounding Double Multiply Add/Subtract
PMULL	Optional Cryptographic and CRC32 Instructions
JSCVT	Perform Conversion to Match Javascript
DCPOP	Persistent Memory Support

IOMMU Features

Feature name	Description
enabled	IOMMU is present and enabled in the kernel

Kernel Features

Feature	Attribute	Description
config	<option name>	Kernel config option is enabled (set 'y' or 'm'). Default options are NO_HZ, NO_HZ_IDLE, NO_HZ_FULL and PREEMPT
selinux	enabled	Selinux is enabled on the node
version	full	Full kernel version as reported by /proc/sys/kernel/osrelease (e.g. '4.5.6-7-g123abcde')
	major	First component of the kernel version (e.g. '4')
	minor	Second component of the kernel version (e.g. '5')
	revision	Third component of the kernel version (e.g. '6')

Kernel config file to use, and, the set of config options to be detected are configurable. See configuration options for more information.

Local (User-specific Features)

NFD has a special feature source named local which is designed for running user-specific feature detector hooks. It provides a mechanism for users to implement custom feature sources in a pluggable way, without modifying nfd source code or Docker images. The local feature source can be used to advertise new user-specific features, and, for overriding labels created by the other feature sources.

The local feature source tries to execute files found under /etc/kubernetes/node-feature-discovery/source.d/ directory. The hooks must be available inside the Docker image so Volumes and VolumeMounts must be used if standard NFD images are used.

The hook files must be executable. When executed, the hooks are supposed to print all discovered features in stdout, one feature per line. Hooks can advertise both binary and non-binary labels, using either <name> or <name>=<value> output format.

Unlike the other feature sources, the name of the hook, instead of the name of the feature source (that would be local in this case), is used as a prefix in the label name, normally. However, if the <name> printed by the hook starts with a slash (/) it is used as the label name as is, without any additional prefix. This makes it possible for the hooks to fully control the feature label names, e.g. for overriding labels created by other feature sources.

The value of the label is either true (for binary labels) or <value> (for non-binary labels). stderr output of the hooks is propagated to NFD log so it can be used for debugging and logging.

An example:
User has a shell script /etc/kubernetes/node-feature-discovery/source.d/my-source which has the following stdout output:

MY_FEATURE_1
MY_FEATURE_2=myvalue
/override_source-OVERRIDE_BOOL
/override_source-OVERRIDE_VALUE=123

which, in turn, will translate into the following node labels:

feature.node.kubernetes.io/my-source-MY_FEATURE_1=true
feature.node.kubernetes.io/my-source-MY_FEATURE_2=myvalue
feature.node.kubernetes.io/override_source-OVERRIDE_BOOL=true
feature.node.kubernetes.io/override_source-OVERRIDE_VALUE=123

NOTE! NFD will blindly run any executables placed/mounted in the hooks directory. It is the user's responsibility to review the hooks for e.g. possible security implications.

Memory Features

Feature name	Description
numa	Multiple memory nodes i.e. NUMA architecture detected

Network Features

Feature	Attribute	Description
sriov	capable	Single Root Input/Output Virtualization (SR-IOV) enabled Network Interface Card(s) present
	configured	SR-IOV virtual functions have been configured

PCI Features

Feature	Attribute	Description
<device label>	present	PCI device is detected

The <device label> part is composed of raw PCI IDs, separated by dashes. The set of fields used in <device label> is configurable, valid fields being class, vendor, device, subsystem_vendor and subsystem_device. Defaults fields are class and vendor. An example label using the default label fields:

feature.node.kubernetes.io/pci-1200_8086.present=true

Also the set of PCI device classes that the feature source detects is configurable. By default, device classes (0x)03, (0x)0b40 and (0x)12, i.e. GPUs, co-processors and accelerator cards are deteted.

See configuration options for more information on NFD config.

RDT (Intel Resource Director Technology) Features

Feature name	Description
RDTMON	Intel RDT Monitoring Technology
RDTCMT	Intel Cache Monitoring (CMT)
RDTMBM	Intel Memory Bandwidth Monitoring (MBM)
RDTL3CA	Intel L3 Cache Allocation Technology
RDTL2CA	Intel L2 Cache Allocation Technology
RDTMBA	Intel Memory Bandwidth Allocation (MBA) Technology

Storage Features

Feature name	Description
nonrotationaldisk	Non-rotational disk, like SSD, is present in the node

System Features

Feature	Attribute	Description
os_release	ID	Operating system identifier
	VERSION_ID	Operating system version identifier

Getting started

System requirements

1. Linux (x86_64/Arm64)
2. [kubectl] kubectl-setup (properly set up and configured to work with your Kubernetes cluster)
3. [Docker] docker-down (only required to build and push docker images)

Usage

Feature discovery is preferably run as a Kubernetes DaemonSet. There is an example spec that can be used as a template, or, as is when just trying out the service:

kubectl create -f rbac.yaml
kubectl create -f node-feature-discovery-daemonset.yaml.template

When the job runs, it contacts the Kubernetes API server to add labels to the node to advertise hardware features.

If you have RBAC authorization enabled (as is the default e.g. with clusters initialized with kubeadm) you need to configure the appropriate ClusterRoles, ClusterRoleBindings and a ServiceAccount in order for NFD to create node labels. The provided templates will configure these for you.

When run as a daemonset, nodes are re-labeled at an interval specified using the --sleep-interval option. In the template the default interval is set to 60s which is also the default when no --sleep-interval is specified.

Feature discovery can alternatively be configured as a one-shot job. There is an example script in this repo that demonstrates how to deploy the job in the cluster.

./label-nodes.sh

The label-nodes.sh script tries to launch as many jobs as there are Ready nodes. Note that this approach does not guarantee running once on every node. For example, if some node is tainted NoSchedule or fails to start a job for some other reason, then some other node will run extra job instance(s) to satisfy the request and the tainted/failed node does not get labeled.

Configuration options

NFD supports a configuration file. The default location is /etc/kubernetes/node-feature-discovery/node-feature-discovery.conf, but, this can be changed by specifying the--config command line flag. The file is read inside the Docker image, and thus, Volumes and VolumeMounts are needed to make your configuration available for NFD. The preferred method is to use a ConfigMap. For example, create a config map using the example config as a template:

cp node-feature-discovery.conf.example node-feature-discovery.conf
vim node-feature-discovery.conf  # edit the configuration
kubectl create configmap node-feature-discovery-config --from-file=node-feature-discovery.conf

Then, configure Volumes and VolumeMounts in the Pod spec (just the relevant snippets shown below):

...
  containers:
      volumeMounts:
        - name: node-feature-discovery-config
          mountPath: "/etc/kubernetes/node-feature-discovery/"
...
  volumes:
    - name: node-feature-discovery-config
      configMap:
        name: node-feature-discovery-config
...

You could also use other types of volumes, of course. That is, hostPath if different config for different nodes would be required, for example.

The (empty-by-default) example config is used as a config in the NFD Docker image. Thus, this can be used as a default configuration in custom-built images.

Configuration options can also be specified via the --options command line flag, in which case no mounts need to be used. The same format as in the config file must be used, i.e. JSON (or YAML). For example:

--options='{"sources": { "pci": { "deviceClassWhitelist": ["12"] } } }'

Configuration options specified from the command line will override those read from the config file.

Currently, the only available configuration options are related to the PCI and Kernel feature sources.

Building from source

Download the source code.

git clone https://github.com/kubernetes-sigs/node-feature-discovery

Build the container image:

cd <project-root>
make

NOTE: Our default docker image is hosted in quay.io. To override the QUAY_REGISTRY_USER use the -e option as follows: QUAY_REGISTRY_USER=<my-username> make image -e

You can also specify a build tool different from Docker, for example:

make IMAGE_BUILD_CMD="buildah bud"

Push the container image (optional, this example with Docker)

docker push <quay-domain-name>/<registry-user>/<image-name>:<version>

Change the job spec to use your custom image (optional):

To use your published image from the step above instead of the quay.io/kubernetes_incubator/node-feature-discovery image, edit image attribute in the file node-feature-discovery-job.yaml.template to the new location (<quay-domain-name>/<registry-user>/<image-name>[:<version>]).

Targeting Nodes with Specific Features

Nodes with specific features can be targeted using the nodeSelector field. The following example shows how to target nodes with Intel TurboBoost enabled.

apiVersion: v1
kind: Pod
metadata:
  labels:
    env: test
  name: golang-test
spec:
  containers:
    - image: golang
      name: go1
  nodeSelector:
    feature.node.kubernetes.io/pstate-turbo: 'true'

For more details on targeting nodes, see node selection.

References

Github issues

• #28310
• #28311
• #28312

Design proposal

Governance

This is a SIG-node subproject, hosted under the Kubernetes SIGs organization in Github. The project was established in 2016 as a Kubernetes Incubator project and migrated to Kubernetes SIGs in 2018.

License

This is open source software released under the Apache 2.0 License.

Demo

A demo on the benefits of using node feature discovery can be found in demo.