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A blueprint family which showcases end-to-end solution for edge services with KubeEdge centered edge stack. The first release will focus on the ML inference offloading use case. 

KubeEdge is a CNCF sandbox project widely adopted as a key industry reference Edge Computing architecture. With KubeEdge, application vendors can push a wide range of applications at the edge, including Mobile Edge services. It is an upstream project to Akraino community. There are other Akraino Blueprint projects with KubeEdge incorporated. This project family will focus on solutions centered around KubeEdge. Future releases may incorporate various components, e.g. hardware infrastructure etc. 

Use Case Details

Attributes

Description

Informational

Type

New


Industry Sector

Cloud, Enterprise, Telco


Business driver

Edge computing leverages edge locations to distribute application loads among device/edge/cloud. A service layer is required to bridge infrastructure platform and applications. e.g. load distribution coordination, hardware platform agnostic, etc. KubeEdge extends native containerized application orchestration capabilities to hosts at Edge. Along with other vertical domain support such as device twin at edge, KubeEdge edge service stack is geared to offer feature rich support to applications while remain platform neutral.    


Business use cases

KubeEdge Edge service can be deployed at enterprise edge or as a cloud edge extension interfacing telco network. It offers support for following use cases:

  • ML offloading for inference and training in image recognition for mobile phones
  • Automatic Speech Recognition (ASR) in operation field
  • Manufacture production line defect inspection
  • IoT gateway
  • Mobile Edge enabler  


Business Cost - Initial Build Cost Target Objective

KubeEdge is a software layer. Its managed applications can run on any kubernetes environment. Validated edge stack including hardware choices should have manageable cost suitable for edge deployment.  


Business Cost – Target Operational Objective

KubeEdge edge service provides service portal for operational management. It supports zero touch deployment and monitoring capabilities. 


Security need

KubeEdge supports application oriented security SPIFFE spec.


Regulations

N/A


Other restrictions

N/A


Additional details

N/A


Blueprint Family Details

Use Case Attributes

Description

Informational

Type

New


Blueprint Family

KubeEdge Edge Service


Use Case

Telco edge and enterprise edge


Blueprint proposed

Central Office deployments

•  ML inference offloading

Customer Premise deployments

•  ASR at operation field (future proposal)


Initial POD Cost (capex)

Less than USD100K


Scale

From 1 server to a rack.


Applications

Any type of edge services


Power Restrictions

Varies


Preferred Infrastructure orchestration

OpenStack - VM orchestration

Docker/K8 - Container Orchestration

OS - Linux

VNF Orchestration - ONAP


Additional Details

N/A


Blueprint Details

Case Attributes

Description

Informational

Type

New


Blueprint Family

KubeEdge Edge Service


Use Case

Facial emotion recognition task offloading to edge node


Blueprint proposed Name

ML Inference Offloading


Initial POD Cost (capex)

Less than 100KUSD


Scale & Type

1 x86 server

With Nvidia Tesla P4/T4 GPUs


Applications

Deep learning models(facial expression) offload from mobile device to Edge


Power Restrictions

Varies


Infrastructure orchestration

Docker 18.09

OS – Ubuntu18.04

Python 3.5 ~3.7

CUDA>10.1

GPU driver release 19.03


PaaS

KubeEdge, Kubernetes


SDN

N/A

Workload Type

Containers


Additional Details

N/A

Committers

As per the Akraino Community process and directed by TSC, a blueprint which has only one nominee for Project Technical Lead (PTL) will be the elected lead once at least one committer seconds the nomination after the close of nominations.  If there are two or more, an election will take place.

Self Nomination began on 23 April 2020 and will continue until 29 April.

Committer

Committer

Company

 Committer Contact Info

Committer Bio

Committer Picture

Self Nominate for PTL (Y/N)

Jane ShenFuturewei jane.shen@futurewei.com


Yin DingFutureweiyin.ding@futurewei.com

Y
Tina TsouARMtina.tsou@arm.com


Xuan JiaChina Mobilejiaxuan@chinamobile.com


Jiafeng ZhuFutureweijiafeng.zhu@futurewei.com


Hanyu DingChina Mobiledinghanyu@chinamobile.com


Jeff BrowerSignalogicjbrower@signalogic.com








Contributors

Contributor

Contributor

Company

 Contributor Contact Info

Contributor Bio

Contributor Picture

May Chen















Akraino KubeEdge Edge Service Blueprint.pptx



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