Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

Overview

As member of Akraino's Kubernetes-Native Infrastructure family of blueprints, the Industrial Edge (IE) blueprint leverages the best-practices and tools from the Kubernetes community to declaratively manage edge computing stacks (i.e. all infrastructure, clusters, and services) at scale and with a consistent, uniform user experience.

The Industrial Edge blueprint addresses a common use case in manufacturing which is "predictive maintenance", the detection of anomalies in sensor data coming from production line servers to be able to schedule maintenance and avoid costly downtimes. Anomaly detection is based on machine learning inference on streaming sensor data.

The 3-node, highly-available factory edge clusters produced by this blueprint are manageble via a central management hub running Open Cluster ManagementThe management hub cluster also hosts OpenDataHub, which allows streaming data mirrored from factory edge clusters to be stored in a data lake for re-training of machine learning models and deploying updated models back to the factory sites. OpenDataHub includes Jupyter Notebooks for data scientists to analyse data and work on models.

Documentation

User Documentation for KNI Blueprints

KNI IE Architecture

KNI IE Installation Guide

KNI IE Test document

Project Team

Member

Company

Contact

RolePhoto & Bio 

Project Committers detail:

Initial Committers for a project will be specified at project creation. Committers have the right to commit code to the source code management system for that project.

A Contributor may be promoted to a Committer by the project’s Committers after demonstrating a history of contributions to that project.

Candidates for the project’s Project Technical Leader will be derived from the Committers of the Project. Candidates must self nominate by marking "Y" in the Self Nominate column below by Jan. 16th. Voting will take place January 17th.

Only Committers for a project are eligible to vote for a project’s Project Technical Lead.

Please see Akraino Technical Community Document section 3.1.3 for more detailed information.

Committer

Committer

Company

Committer

Contact Info

 Committer BioCommitter Picture 

Self Nominate for PTL (Y/N)

Frank Zdarsky

Red Hat

  Jennifer KoervIntelCommitterEdge Computing Team Lead, Emerging Technologies, Office of the CTOIntel Open Source Technology Center – Edge Arch and Pathfinding
Andrew BaysRed HatCommitterLeif
MadsenYolanda RoblaRed HatYolanda Robla MotaCommitterLeif MadsenRed Hat NFVPE – DevOps and Automation Team Lead

Tapio Tallgren

Nokia

  Justin ScottIntelIntel Open Source Technology Center- Edge Arch and Pathfinding

Overview

Project contributors:

Project committers:

  • to be identified once the proposal is accepted

Project plan:

  • to be developed once the proposal is accepted

Resourcing:

  • will be established once the proposal is accepted

...

- Edge, baremetal provisioning
Ricardo NoriegaRed HatRicardo Noriega

PTL

Principal Software Engineer, Emerging Technologies, Office of the CTO

Abhinivesh JainWiproAbhinivesh JainCommitterDistinguished Member of Technical Staff, CTO office

Project Templates

Use Case Template

Attributes

Description

Informational

Type

New

 


Industry Sector

Manufacturing, Energy 


Business Driver 



Business Use Cases 



Business Cost - Initial Build Cost Target Objective

 



Business Cost – Target Operational Objective

 



Security Need

 



Regulations 



Other Restrictions

 



Additional Details



Blueprint Template

Attributes

Description

Informational

Type

New 


Blueprint Family - Proposed Name

Kubernetes-Native Infrastructure for Edge (KNI-Edge) 


Use Case

Industrial Edge (IE) 


Blueprint - Proposed Name

Industrial Edge (IE) 


Initial POD Cost (CAPEX)

(TBC)

 


Scale & Type

3 servers to 1 rack; x86 servers (Xeon class)

 


Applications

IoT Cloud Platform, Analytics/AI/ML, AR/VR, ultra-low latency control

 


Power Restrictions

(TBC) 


Infrastructure orchestration

End-to-end Service Orchestration: n/a
Middlewares: Knative (serverless), Kubeflow (AI/ML), EdgeX (IoT)
App Lifecycle Management: Kubernetes Operators (mix of Helm and native)
Cluster Lifecycle Management: Kubernetes Cluster API/Controller
Cluster Monitoring: Prometheus
Container Platform: Kubernetes (OKD 4.0)
Container Runtime: CRI-O
VM Runtime: KubeVirt
OS: CoreOS, CentOS-rt

 



SDN

OVN 


SDSCeph

Workload Type

containers, VMs 


Additional Details