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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.

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Committer

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Committer

Company

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Committer

Contact Info

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Self Nominate for PTL (Y/N)

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 

Frank Zdarsky

Red Hat

Jennifer KoervIntelIntel Open Source Technology Center – Edge Arch and PathfindingCommitterEdge Computing Team Lead, Emerging Technologies, Office of the CTO
Andrew BaysRed Hat

Tapio Tallgren

Nokia

David LyleIntelDavid LyleIntel Open Source Technology Center- Edge Arch and PathfindingMikko YlinenIntelMikko YlinenIntel Open Source Technology Center- Edge Arch and PathfindingNed SmithIntelNed SmithIntel Open Source Technology Center- Edge Arch and Pathfinding; Security, Trusted Computing, Privacy, Safety.Committer
Yolanda RoblaRed HatYolanda Robla MotaCommitterRed Hat NFVPE - Edge, baremetal provisioningY
Ricardo NoriegaRed HatRicardo Noriega De Soto

Red Hat NFVPE - CTO office

Networking

Manjari AsawawiproManjari Asawa <manjari.asawa@wipro.com>

PTL

Principal Software Engineer, Emerging Technologies, Office of the CTO

Abhinivesh JainWiproAbhinivesh JainCommitterDistinguished Member of Technical Staff, CTO office

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

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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



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