Sno | Name | Organization | Module | |
---|---|---|---|---|
1 | Jaegon Kim | Samsung Electronics Co., Ltd, Korea | SAFe Framework Design | jaegon77.kim@samsung.com |
2 | Karthikeyan Subramaniam | Samsung R&D Institute of India | Netflow | karthikeyan.s@samsung.com |
3 | Saritha Ramesh Thangaraju | Samsung R&D Institute of India | Analytics Manager, Usecase Workflows | |
4 | Senthil Subramaniam | Samsung R&D Institute of India | Sflow, Data Monitor, Usecase Workflows | |
5 | Sudhakar B | Samsung R&D Institute of India | AL/ML Adapter, Usecase Workflows | |
6 | Tae woo Kim | Samsung Electronics Co., Ltd, Korea | SAFe Framework Design | t01.kim@samsung.com |
Irrespective of underlying network technologies, the network can be intelligently controlled with Software Defined Networking(SDN). The SDN control software can manage various network elements including switches, routers, and virtual switches agnostic to vendors. The network administrators use SDN for rapid deployment including configuration, monitoring, and troubleshooting devices across SDN-controlled networks. SDN is being adapted at a faster pace to accommodate the evergrowing Network traffic needs. The Network administrator spends a considerable amount of time in repetitive activities like Network Software upgrades, Monitoring, Troubleshooting, etc. Typically, the SDN controller does not analyze traffic conditions which are required for the network administrators to optimize resource utilization, service quality, anomaly detection and etc. To address this issue, we are proposing to introduce Smart Automation Framework (SAFe) in ONOS. We also investigated a few issues like Network devices software upgrades and Resource Utilization. Our investigation proved that the SAFe improves operational efficiency (i.e., minimal or zero traffic loss, less operational cost) when compared to the manual procedure. The SAFe can handle workflow-based applications like optimal resource monitoring, anomaly detection, AI/ML-driven configuration, etc.
This document describes the AI/ML-assisted Smart Automation Framework for ONOS, including its design, implementation, and operation. The purpose of this framework is to enable intelligent automation and reduce operational costs. Automation can be anything like Identifying Network devices by ZTP, Configuring based on the type of network device, applying QOS policies, routing, monitoring and etc.
The SAFe can be integrated with third-party AI/ML tools. The third-party integration can be enabled by AI-as-a-service. AI-as-a-service is an open-source middleware AI agent.
The automation workflows can be written using this framework, for various AI based usecases.
The following illustrates the directory structure:
ai-plugin
+-- api
| +-- main
| | +-- aiadapter
| | +-- analyticsmanager
| | +-- datamonitor
|
| +-- test
| | +-- aiadapter
| | +-- analyticsmanager
| | +-- datamonitor
|
+-- app
| +-- main
| | +-- aiadapter
| | +-- analyticsmanager
| | +-- datamonitor
| | +-- cli
|
| +-- test
| |+-- aiadapter
| |+-- analyticsmanager
| |+-- datamonitor
|
The app directory contains subdirectories
The datamonitor subdirectory contains the following subdirectories, providing a number of categories of functionality:
The datamonitor subdirectory contains the following subdirectories, providing a number of categories of functionality:
The datamonitor subdirectory contains the following subdirectories, providing a number of categories of functionality:
The mladapter subdirectory contains the following subdirectories, providing a number of categories of functionality:
Events listener
The datamonitor subdirectory contains the following subdirectories, providing a number of categories of functionality:
The mladapter subdirectory contains the following subdirectories, providing a number of categories of functionality:
This proposed Smart Automation Framework(SAFe) is used for the following use cases
datamonitor - Datacollector, Cli, Datamanager
mladapter - Collector Manager
datamonitor[test] - datacollector, cli, datamanager
mladapter - Events Manager
analytics manager
https://jira.onosproject.org/browse/ONOS-8163