Table of Contents
Blueprint overview/Introduction
The AI Edge is an Akraino approved blueprint family and part of Akraino Edge Stack, which intends to provide an open source MEC platform combined with AI capacities at the Edge, and could be used for safety, security, and surveillance. The MEC platform, which named ote-stack, targets on shielding the heterogeneous characteristics through underlying hardware virtualization and providing an unified access for cloud edge, mobile edge and private edge. In addition, the AI Edge utilizes the cluster management and intelligent scheduling of multi-tier clusters to enable low-latency, high-reliability and cost-optimal computing support for running AI applications at the edge. At the same time, it makes device-edge-cloud collaborative computing possible.
This blueprint mainly focuses on building an edge federated ML platform to implement federated ML algorithms on servers in the edge.
Use Case
<use case 1: Federated Learning in Privacy Protection>
...
Federated learning provides a safe tunnel across different medical institutions like hospitals and clinics, so that they can use train their own AI model with the data from multi-sources and without worry about the law issues and privacy problem with the help of Federated learning.
Where on the Edge
Business Drivers
The AI Edge will provide a cluster management for different logical MEC edge clusters. Through the standard api interface, the third clusters can join the management of AI Edge easily, so as to schedule deployment of an AI application to a specific edge node with the unified access. The benefits are: Lower cost on manage multiple edge clusters and more computing power of edge devices can be utilized ; Less load and latencies on network and more safely since the application is running locally; Edge cluster autonomy.
Overall Architecture
The AI Edge blueprint architecture consists of a cluster control manager with web platform at the cloud and multiple edge clusters. The number of clusters can be theoretically unlimited which can effectively solve the management and scheduling problems of large-scale mobile edge clusters in 5G era. For development environment we have tested with one IEC clusters with 3 nodes.
...
The below image shows the overall architecture for using IEC as edge infrastructure in AI Edge.
Platform Architecture
The detailed platform architecture of AI Edge blueprint is shown in the below diagram.
...
Other components, such as openapi, ote-web, are currently released as docker images and will be open source in the future.
Software Platform Architecture
The below image shows the software architecture for this release.
...
Data flow between and within devices.
APIs
R5 Federated ML application at edge API Document
Hardware and Software Management
Software Management: Gerrit Repo
Licensing
Apache 2.0 license