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

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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

node-feature-discovery.

  Usage:
  node-feature-discovery [--no-publish --sources=<sources> --label-whitelist=<pattern>]
  node-feature-discovery -h | --help
  node-feature-discovery --version

  Options:
  -h --help                   Show this screen.
  --version                   Output version and exit.
  --sources=<sources>         Comma separated list of feature sources.
                              [Default: cpuid,rdt,pstate,network]
  --no-publish                Do not publish discovered features to the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: ]

Feature discovery

Feature sources

The current set of feature sources are the following:

• CPUID for x86 CPU details
• Intel Resource Director Technology
• Intel P-State driver
• Network

Feature labels

The published node labels encode a few pieces of information:

• A "namespace" (e.g. node.alpha.kubernetes-incubator.io/nfd).
• The version of this discovery code that wrote the label, according to git describe --tags --dirty --always.
• 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).

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

{
  "node.alpha.kubernetes-incubator.io/node-feature-discovery.version": "v0.1.0",
  "node.alpha.kubernetes-incubator.io/nfd-cpuid-<feature-name>": "true",
  "node.alpha.kubernetes-incubator.io/nfd-rdt-<feature-name>": "true",
  "node.alpha.kubernetes-incubator.io/nfd-pstate-<feature-name>": "true",
  "node.alpha.kubernetes-incubator.io/nfd-network-<feature-name>": "true"
}

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.

Intel Resource Director Technology (RDT) Features

Feature name	Description
RDTMON	Intel Cache Monitoring Technology (CMT) and Intel Memory Bandwidth Monitoring (MBM)
RDTL3CA	Intel L3 Cache Allocation Technology
RDTL2CA	Intel L2 Cache Allocation Technology

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)

Network Features

Feature name	Description
SRIOV	Single Root Input/Output Virtualization (SR-IOV) enabled Network Interface Card

Getting started

System requirements

1. Linux (x86_64)
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 done as a one-shot job. There is an example script in this repo that demonstrates how to deploy the job to unlabeled nodes.

./label-nodes.sh

The discovery script will launch a job on each unlabeled node in the cluster. When the job runs, it contacts the Kubernetes API server to add labels to the node to advertise hardware features (initially, from cpuid, RDT, p-state and network).

Building from source

Download the source code.

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

Build the Docker 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 docker -e

Push the Docker Image (optional)

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 line 40 in the file node-feature-discovery-job.json.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": {
                "node.alpha.kubernetes-incubator.io/nfd-pstate-turbo": "true"
        }
    }
}

For more details on targeting nodes, see node selection.

References

Github issues

• #28310
• #28311
• #28312

Design proposal

Kubernetes Incubator

This is a Kubernetes Incubator project. The project was established 2016-08-29. The incubator team for the project is:

• Sponsor: Dawn Chen (@dchen1107)
• Champion: David Oppenheimer (@davidopp)
• SIG: sig-node

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.